The, A Linguistic Anthropologist, There Are Many...

The, A Linguistic Anthropologist, There Are Many...
As a linguistic anthropologist, there are many inferences we can make about the type of place this
lost tribe lived based on a few key phrases in their language. For instance, we can infer that because
they have no word for ocean, they lived in an environment that was inland. Based on the statement
that the only terrain this tribe knew of was absolutely flat, rolling and slightly hilly, we can presume
two conclusions: They were not aware of mountains, and they had a small radius of land in which
they traveled. If one travels in any direction for a long enough time, one is bound to find either an
ocean or a mountain, so the fact they did not know what either of these were insinuates that they
may not have traveled very much. A reason for this could be that they wanted to preserve the climate
they had. If you combine the information that says dozens of words exist for grain, including eight
for wheat alone, that they had no words for beef, pork, veal, leather or mutton, and that there are
several words for precipitation, most of which translate to rain, we can presume that it is likely that
they may not have traveled from their home land very much, because they did not know if there
would be rain in places they would travel to. Rain may have been a key element to their survival, so
they did not want to leave a place that had it. Building upon the presumptions I have made, I will
attempt to analyze what this lost tribes values and beliefs may have been. As I said previously,
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Analysis Of The Story ' Folie A Deux ' By William Trevor
The story "Folie a Deux" by William Trevor explores the complexities of curiosity and maturation.
The narrator, inspired by literary devices, details an important passage of the story, which portrays
lunchtime conversations between Anthony, his father, and Miss Davally, and follows with the
correspondence between Miss Davally and Wilby's mother – where all involve the exchange of
information. The passage is meaningful because it demonstrates an appraisal of information, and its
impact on progress. Ultimately, the story suggests that information is arbitrarily powerful, and that
genuine maturation is only fostered through nurtured curiosity.
The portrayal of lunchtime conversations suggests information is invaluably powerful and depends
on human connections to establish a worldview. While everyone is eating, the narrator describes that
"much other information (is) passed on at the lunch table", and details the questioned functionality
of "aeroplanes", "time", and "spiders" (132). The narrator's description of how information is
situated at "the lunch table" implicitly suggests human connections to be an important survival need,
like nourishment or "lunch", and infers their interdependency. Through the phrase "much other
information was passed on", the narrator conveys the abundantly–distinct richness of information,
which is inferred by the connotations "much" and "other". The description that information "was
passed on" at the table signifies the table to be the center of

Human Activities Like Dam Construction
Motivation and Objective
Human activities like dam construction, dredging, and agricultures cause large amount of sediment
transports in rivers, lakes, and estuaries. Erosion and sedimentation is a global issue that tends to be
primarily associated with water quality. Pollution by sediment has two major types. For a physical
dimension, erosion leads to excessive levels of turbidity in waters and the turbidity limits
penetration of sunlight thereby prohibiting growth of algae and rooted aquatic plants. High levels of
sedimentation lead to physical disruption of the hydraulic characteristics of the channel which have
serious impacts on reduction in channel depth, and it can cause increased flooding. For a chemical
dimension, the silt and clay fraction (<62mm) is a primary carrier of adsorbed chemicals originated
from agricultures like phosphorus, chlorinated pesticides and most metals transported into the
aquatic system. The use of numerical hydrologic, hydraulic, and sediment transport models has
greatly expanded to predict and interpret behavior of erosion and sediment runoff for controlling
sediment pollutant and keeping water resources safe. Unfortunately, predictions from such models
always contain uncertainty, and the overall uncertainty is poorly quantified and deterministic
predictions have been used in most applications. Because those predictions are often used in
situations that involve the potential for economic losses, ecological impacts, and risks to human

Relational Reasoning Tested Through Analogies
Relational Reasoning Tested Through Analogies
Relational reasoning is when inferences are made based on relational roles and the patterns they
create (Holyoak, 2012). This capability is considered a fundamental ability in cognitive processing
(Alexander, Dumas, Grossnickle, List & Firetto, 2015), and represents how humans can to explain
and expand on problems they face (Krawczyk, 2012). An example of relational reasoning can be
found in series completion tasks. An example of a series completion task is to extend a pattern of
digits. To complete this, recognition of the relations between the digits, as well as integrating these
patterns is required. If this pattern is recognised, then it can be applied to complete the problem.
A key ... Show more content on Helpwriting.net ...
The complete experiment was conducted in two separate sub–tests. Firstly, a test allowing
participants to evaluate whether the given analogy solutions were correct or incorrect. The second
test allowed participants to generate solutions to incomplete analogies. The results for the first test
provided no significant difference between near and distant analogies in relational reasoning. The
second experiment's results concluded there was significantly more relational reasoning in the
distant analogy condition than in the near analogy condition.
The current study will focus on whether relational reasoning can be promoted through a different
task other than in analogies. Relational reasoning will be investigated through an n–term task, which
required three conditions: control, near analogy, and distant analogy conditions.
The current study expects to conclude that in the control condition, the 3–term items will be solved
more accurately than the 4–term items – this is based off Vendetti et al.'s (2014) study. The expected
results for the analogy conditions are that n–term accuracy will be higher in the distant analogy
condition than the near analogy condition, and that there will be no difference between the 3–term
and 4–term premises.
Method
Participants
The experiment included a total of 151 participants (42 male; 109 female), all of which are enrolled
in a Griffith University second year Psychology course.

Who Are Empiricists?
Empiricists must prove through experience that we gain through our senses that we have personal
identities. The problem is what senses are we considering as experience for our identity. Empiricists
must establish if they are looking at identity as the mind, body, a combination of both, or none.
There is a fundamental disagreement between Locke and Hume. Locke believes that our identities
are the connection of observations of ourselves through our memories. However, Hume does not
believe that there is a necessary connection between causes and effects; since he believes that our
ideas 'connection' to each other is consequential, then identify defined as consciousness cannot exist.
To Locke, an individual's existence is the connections between ... Show more content on
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The problem is he does not prove it. As an empiricist, he should have to be able to prove personal
identity through experience, but what we are left with is Descartes "I think, therefore I am." Locke's
answer does not solve the problem of what makes me who I am. In the other hand, Hume essentially
acknowledges that we cannot prove personal identity, that there is no possible way to prove identity
empirically. Since we connect facts because of socialization and information from memory and
senses (22), we can only infer that we have an identity. The big problem is that empiricists believe
that we can prove everything through experience. However, whose experience of senses should I
take as evidence for my identity? Mine or how other people experience me? Empiricism has the
fundamental issue of contradicting experiences resulting in some questions left

Report: Inferring Strategy
1. In general, how successful was the lesson? Did the students learn what you intended for them to
learn? How do you know?
This lesson was moderately successful. I believe that, as a result of this lesson, the students were
able to develop a decent understanding of the inferring strategy. Prior to this lesson, the students
displayed a basic understanding of the strategy but after this lesson, the students demonstrated a
better understanding of the lesson. The students completed a graphic organizer as we read the text
and they were asked make one inference independently. After collecting and reviewing the
organizers completed, I noticed that more students were able to make an inference that used prior
knowledge and information from the text. ... Show more content on Helpwriting.net ...
Comment on different aspects of your instructional delivery (e.g. activities, grouping of students,
materials, and resources). To what extent were they effective?
The students are more engaged when we use the SMART board. They participate more and pay
closer attention to the lesson because they want to go up to the SMART board. Even though we had
technical difficulties and quite a bit of chatting, the students still appeared to be engaged in the
lesson. I had the students discuss in their groups the word that matched the definition. The students
groups are made up of mixed abilities.
In terms of material, I decided to select a text that would be more interesting to the students. The
Theme in the Literacy by Design text book is animals so the text is non–fiction. Non–fiction text is
more direct so it is a little tricky to make an inference. I used a grade appropriate novel that the
students could make inferences. The brother in the story has autism but the narrator does not directly
say that the brother has autism at first. The novel, compared to the text in the source book, gave the
students a better opportunity to make inferences.
6. If you had a chance to teach this lesson again to the same group of students, what would you do

Types Of Common Intention Constructive Trusts
For sole name cases, there are two distinctive stages where claimant have to establish a beneficial
interest under constructive trust before quantifying the beneficial interest whereas in joint name
cases, it is assumed that there is joint equitable ownership (i.e. 50:50 split) where the court only has
to quantify the beneficial interest.
For single name cases like Lloyds, claimant should acquire beneficial interest before it can be
quantified. There are two types of common intention constructive trusts: (i) express common
intention constructive trust and (ii) inferred (implied) common intention constructive trust. Lord
Bridge made two distinctions, where he referred the first category to constructive trust and
proprietary estoppel whereas the second category to constructive trust.
As regards (i), beneficial interest in a family home can be claimed through an oral agreement
between the couple, where claimant relied on the agreement to his or her detriment. A lie can be
considered as an oral agreement. However, it must have been communicated to each other. Instead
of legal language, the courts are looking for language that forms the necessary intention on which to
base a proprietary interest. Intention of parties must be common where Dilon LJ expressed that if
parties' intention was not expressed, 'common' intention would be difficult to prove.
Regarding detrimental reliance, claimant must prove that he or she relied on the agreement to his or
her detriment. To

A Reflection Of Human Setting In Into The Wild And The...
Novels and storybooks comprise a reflection of human character and individuals in the society. They
are a mirror through which the audience is able to infer lessons which promote their sustainability in
life. Similarly, films are a reflection of a particular viewpoint in the society. Conversely, the film
Into the Wild and the novel Tracks offer insight on an element of human life. They explore the
human setting and collectively conclude that we should all try to escape society. Principally, based
on the premises set forth by the film and the novel, it is our inherent responsibility to isolate
ourselves from those who are around us.
To begin, the theme of isolation comprises a pervasive element in both the novel and the film.
Isolation refers to the distancing of oneself from their communities or peers. In In to the Wild the
main character, Christopher, commits himself to a journey through the isolated wild (Penn 2007). He
renounces his previous affiliations and sets out into the wild with nothing but a journal on existence
in the wild. In the journal, he views himself as a heroic loner and thus contends: "You can do
anything. You can go anywhere. Money, power is an illusion. It's up here. You can be here. Me and
You" (Penn 2007). His commitment to the wild is predicated on the feelings of freedom that were
triggered upon his access into the wild. Equally, the novel Tracks captures the story of Robyn
Davidson who leaves the comfort of her town in the pursuit of camels in the

Outline Of Explicit Ideas And Information
LA.3.1.7.3, LA.4.1.7.3; The student will determine explicit ideas and information in text, including
main idea, supporting details, implied message and inferences, and chronological order of
events.Read closely to determine what the text says explicitly and to make logical inferences from
it; cite specific textual evidence when writing or speaking to support conclusions drawn from the
text. Grade 3– 4 activity Essential Question(s): What words signal the reader to go beyond what 's
written to what is implied in the text? How do readers infer the meaning of unknown words? How
do readers determine the main idea when it is inferred? How do readers use text clues to make
inferences in an assessment passage? How can inferring and summarizing help readers identify the
main idea and relevant details? Materials/Resources: What do I have or need to teach this lesson
objective(s)? Teacher: My biography, overhead (LCD projector or doc camera), graphic organizer
templates (students can draw these on paper). Student: Paper, pencil, displayed biography, index
cards' and highlighters in three different colors Procedures: 1. Before using text, gather 4–5 related
objects. Display the objects for the class. Share your ideas about the person who might own these
objects. Explain what clues you use to form your conclusion. This conclusion is made by inferring
what you know from personal experience or background knowledge with the objects you have
displayed. 2. Pre–read my biography with the

A & M Research Statement
Research Statement
Nilabja Guha Texas A&M University
My current research at Texas A&M University is in a broad area of uncertainty quantification (UQ),
with applications to inverse problems, transport based filtering, graphical models and online
learning. My research projects are motivated by many real–world problems in engineering and life
sciences. I have collaborated with researchers in engineering and bio–sciences on developing
rigorous uncertainty quantification methods within Bayesian framework for computationally
intensive problems. Through developing scalable and multi–level Bayesian methodology, I have
worked on estimating heterogeneous spatial fields (e.g., subsurface properties) with multiple scales
in dynamical systems. In ... Show more content on Helpwriting.net ...
Some of the areas I have explored in my Ph.D. work include measurement error model with
application in small area estimation, risk analysis of dose–response curves. The stochastic
approximation methods have application in density estimation, deconvolution and posterior
computation. A discussion of my current and earlier projects are given next.
1 UQ for estimating heterogeneous fields
To predict the behavior of a physical system governed by a complex mathematical model depends
on un– derlying model parameters. For example, predicting the contaminant transport or oil
production strongly influenced by subsurface properties, such as permeability, porosity and other
spatial fields. These spatial fields are highly heterogeneous and vary over a rich hierarchy of scales,
which makes the forward models
1
be computationally intensive. The quantities determining the system are partially known and
represent information at some range of spatio–temporal scales. Bayesian modeling is important in
quantifying the un– certainty, identifying dominant scales and features, and learning the system.
Bayesian methodology provides a natural framework for such problems with specifying prior
distribution on the unknown and the likelihood equation. Solution procedure use Markov Chain
Monte Carlo (MCMC) or related methodology, where, for each of the proposed parameter value, we
solve

Essay about Probabilist
'Probabilist' Deductive Inference in Gassendi's Logic*
ABSTRACT: In his Logic, Pierre Gassendi proposes that our inductive inferences lack the
information we would need to be certain of the claims that they suggest. Not even deductivist
inference can insure certainty about empirical claims because the experientially attained premises
with which we adduce support for such claims are no greater than probable. While something is
surely amiss in calling deductivist inference "probabilistic," it seems Gassendi has hit upon a now–
familiar, sensible point–namely, the use of deductive reasoning in empirical contexts, while
providing certain formal guarantees, does not insulate empirical arguments from judgment by the
measure of belief which ... Show more content on Helpwriting.net ...
All these views bear the mark of his distinctively strong empiricism. He proposes (quite reasonably)
that our inductive inferences lack the information we would need to be certain of claims they
suggest, and (a bit more surprisingly) that not even deductivist inference can insure our certainty
about empirical claims because the experientially attained premises we adduce in support of such
claims are no greater than probable.
We might think, on the basis of this last notion, that Gassendi has a good enough seventeenth
century grasp of inductivist logic, and that it's rather deductivist logic he doesn't fully understand.
Yet, while something is surely amiss in calling deductivist inference 'probabilistic', it seems
Gassendi has hit upon a now–familiar, sensible point–that the use of deductive reasoning in
empirical contexts, while providing certain formal guarantees, does not insulate empirical arguments
from judgement by the measure of belief we invest in their premises. Such a view is possible for
Gassendi to begin with because he is among those early Moderns who allow that we may have
warrant for claims though we are not certain of them; this is the 'degrees of belief' concept which
figures prominently in the development of modern probability theory. The more general point,
which distinguishes Gassendi among his contemporaries, is that the strength all empirical claims
share, irrespective of the way we infer them, consists in the

The Key Objective Of A Great Facilitator
The main objective of a great facilitator is to development a team that is cohesive and cooperative,
producing results that make the group the most effective and efficient unit possible. However, this is
not always the case. Building a team that flourishes in any situation can be a daunting endeavor.
Many individuals unknowingly lack the skills necessary to be a great facilitator. Roger Schwarz
(2002), has created nine ground rules for honing a more effective group. When these nine ground
rules are applied and executed by a team, any facilitator will be able to produce a more effective and
efficient group. Furthermore, "by becoming familiar with the ground rules, you can watch a group
and identify specifically what is happening that is enhancing or hindering the group's process"
(Schwarz, 2002 p. 96). Schwarz's (2002), nine ground rules are as followed respectively, "test
assumptions and inferences, share all relevant information, use specific examples and agree on what
important words mean, explain your reasoning and intent, focus on interests, not positions, combine
advocacy and inquiry, jointly design next steps and ways to test disagreements, discuss
undiscussable issues, and us a decision–making rule that generates the level of commitment needed"
(p. 97).
Purpose and Nature of Group
"For a group to optimize its effectiveness, the formal leader cannot perform all of the leadership
functions in all circumstances at all times, and all group members must assist each

Online Learning : Stochastic Approximation
4 Online learning: Stochastic Approximation
Estimating the mixing density of a mixture distribution remains an interesting problem in the
statistics literature. Stochastic approximation (SA) provides a fast recursive way for numerically
maximizing a function under measurement error. Using suitably chosen weight/step–size the
stochastic approximation algorithm converges to the true solution, which can be adapted to estimate
the components of the mixing distribution from a mixture, in the form of recursively learning,
predictive recursion method. The convergence depends on a martingale construction and
convergence of related series and heavily depends on the independence of the data. The general
algorithm may not hold if dependence is present. We have proposed a novel martingale
decomposition to address the case of dependent data.
5 Measurement error model: small area estimation
We proposed [4] a novel shrinkage type estimator and derived the optimum value of the shrinkage
pa– rameter. The asymptotic value of the shrinkage coefficient depends on the Wasserstein metric
between standardized distribution of the observed variable and the variable of interest. In the
process, we also estab– lished the necessary and sufficient conditions for a recent conjecture about
the shrinkage coefficient to hold. The biggest advantage of the proposed approach is that it is
completely distribution free. This makes the estimators extremely robust and I also showed that the
estimator continues to

Comparative Study Of Classification Algorithms
Comparative Study of Classification Algorithms used in Sentiment Analysis
Amit Gupte, Sourabh Joshi, Pratik Gadgul, Akshay Kadam
Department of Computer Engineering, P.E.S Modern College of Engineering
Shivajinagar, Pune amit.gupte@live.com Abstract–The field of information extraction and retrieval
has grown exponentially in the last decade. Sentiment analysis is a task in which you identify the
polarity of given text using text processing and classification. There are various approaches in the
task of classification of text into various classes. Use of particular algorithms depends on the kind of
input provided. Analyzing and understanding when to use which algorithm is an important aspect
and can help in improving accuracy of results.
Keywords– Sentiment Analysis, Classification Algorithms, Naïve Bayes, Max Entropy, Boosted
Trees, Random Forest.
I. INTRODUCTION
In this paper we have presented a comparative study of most commonly used algorithms for
sentimental analysis. The task of classification is a very vital task in any system that performs
sentiment analysis. We present a study of algorithms viz. 1. Naïve Bayes 2.Max Entropy 3.Boosted
Trees and 4. Random Forest Algorithms. We showcase the basic theory behind the algorithms, when
they are generally used and their pros and cons. The reason behind selecting only the above
mentioned algorithms is the extensive use in various tasks of sentiment analysis. Sentiment analysis
of reviews is very common application, the

Thank You Ma Am
Langston Hughes story, "Thank You, Ma'am," Takes place late at night on a darkened street, where
life is tough. Roger is caught trying to steal a woman's pocketbook but when an unexpected act of
kindness from the woman towards a poor boy that brings them together. As the woman cares about
the boy she takes him in to give him food and let clean his look up. The boy does not resist going to
the woman's house. The boy could really use the woman's trust in the future so in the story, he is
trying to build her trust back The theme explored in "Thank You Ma'am" by Langston Hughes is that
when one person goes out of their way to help another, everybody benefits from kindness. The first
reason why the woman takes care of the boy is because she cares about how the boy is treated. She
brings him home to give him food and wash his face, she says "Then it will get washed tonight," this
shows that she was implying that she will bring him home to clean him up. We can infer from the
story that the woman takes care of the boy ... Show more content on Helpwriting.net ...
" "No'm,"said the being dragged boy." This shows that we can infer that the boy wants someone to
treat him well and get feed and cleaned up because at his home that probably doesn't happen often.
The boy has respect for the woman by saying "No'm," which means that he respects her and thinks
it's ok to go home with her. Another reason that we can also infer that the boy feels guilty for trying
to steal the woman's pocketbook and got caught in the process. It seems like he wanted this to
happen for Mrs Jones to take him home and help him. are benefited in this situation because the boy
gets to go home and get cleaned up and has a meal and the woman gets to take care of someone.
This goes back to the claim when one person goes out of their way to help another, everybody is
benefitted, and this proves the

Questions On Lesson Construction Template
ELA6_SB_U4_L8_LC
Lesson Construction Template
Introduction and Objective
[Provide a 3–5–sentence introduction of the concept to be covered in this lesson.]
"I really, really loved the book I just finished!", said Angela. "It was probably the best book I have
ever read!" "Wow," answered Steve. "Why did you like it so much?" To convince Steve that he
should read the book, Angela needs to analyze it for him. In other words, she needs to show him
WHY she liked it so much using specific details and examples. In this lesson, you will learn how to
create an effective analysis for a poem or other text.
Today 's lesson objective is:
Students will develop an analysis using relevant evidence from text(s) to support claims, opinions,
... Show more content on Helpwriting.net ...
Then in 3–4 sentences, explain specifically why you liked it and what you liked about it.
[Insert and image to support the learning skill in this space. Include the hyperlink so it can be
verified as copyright–free.]
Link: https://pixabay.com/en/library–books–tv–multimedia–room–849797/
Instruction, Modeling and Student Activities
[Provide direct instruction and modeling at DOK 1 in this section.]
When a person makes a claim about something, they are stating that what they are saying is true.
An opinion is how a person feels about a particular person, event, or thing.
An inference is a conclusion based on evidence or reasoning.

Claims, opinions, and inferences are all part of analyzing poetry or other texts.
Look at the chart below to help you understand each of the three elements:
Claims need to be backed up by concrete evidence and factual information. For example, if you state
that blue cars are safer in accidents than red cars, you need to be able to prove that. You would need
to provide examples, statistics, studies, or real–life scenarios that can prove your statement as true.
Opinions, on the other hand, do not need to be backed up by fact, because they are your feelings
about something. You might say that you prefer math to reading. Is there a right or wrong answer to
that scenario? Of course not! To create a valid and believable opinion, you would need to have
specific reasons

Reviewer in English Iv Nat
Reviewer in English^
National
Achievement
Test
Reviewer
Prepared by:
Christian Paul A. Jose, IV–St. Lorenzo Ruiz
"Making Inference"
An Inference Defined
In order to knock the verbal section of your standardized test or even the reading portion of your test
in school right out of the ballpark, you need to know what an inference is, first. An inference is an
assumption made based on specific evidence. We make inferences all the time in real life. For
instance, your girlfriend might say to you, "Nice hair," and you could make the inference that she is
being rude because she was smirking when she said it. In life, it's pretty easy to infer the implied
meaning – the meaning not stated directly – because you can use context clues like body ... Show
more content on Helpwriting.net ...
You'll have to actually infer that you need to make an inference about the passage. Sneaky, huh?
Here are a few that require inferencing skills, but don't use those words exactly. * "With which of
the following statements would the author most likely agree?" * "Which of the following sentences
would the author most likely use to add additional support to paragraph three?"
Step 2: Trust the Passage
Now that you're certain you have an inference question on your hands, and you know exactly what
an inference is, you'll need to let go of your prejudices and prior knowledge and use the passage to
prove that the inference you select is the correct one. Inferences on a multiple–choice exam are
different from those in real life. Out in the real world, if you make an educated guess, your inference
could still be incorrect. But on a multiple–choice exam, your inference will be correct because you'll
use the details in the passage to prove it. You have to trust that the passage offers you the truth in the
setting of the test, and that one of the answer choices provided is correct without stepping too far
outside the realm of the passage.
Step 3: Hunt for Clues
Your third step is to start hunting for clues – supporting details, vocabulary, character's actions,
descriptions, dialogue, and more – to prove one of the inferences listed below the question. Take this
question, for example:
Based on the information in the

First Day Of School Plan
2.2 First Day of School Plan
FIRST DAY OF SCHOOL SCHEDULE 8:25am–8:45am *Students will be asked to come in and
begin immediately on morning work to establish a morning routine. Students will be expected to
come in everyday and begin a similar assignment on a level 0.
GREETING/MORNING WORK/SEATING
Greet students at the door, provide parents with list of classroom supplies if needed and confirm
how students are getting home for the day via classroom transportation and parent contact log.
Each child will be asked to place supplies in bins. Once supplies are in bins students will go into the
hallway with book–bags to hang them on assigned hooks. Hooks will have students name on them. .
All supplies needed for the day (paper, pencils, erasers, glue sticks and crayons) will be in a basket
in the middle of the table.)
Student will be asked to sit in desk and eat breakfast quietly on a level 0. Breakfast trash will be
collected. Students will be instructed to put trash on right of desk when finished.
PowerPoint will be displayed featuring a welcome message that states "Welcome to 2nd Grade
Math" Teachers name will also be displayed on the board and on the power point. There will be an
interest inventory assignment on each desk. Students will be asked to complete assignment.
When finished, students will begin to work on math fluency assignment placed on desk.
8:45am–9:05am
*Hand signals will be used to get student's attention quickly to avoid wasting instructional time.

Desiree 's Baby, By Kate Chopin
In Kate Chopin's short story, Desiree's Baby, she tells the story through the eyes of the characters.
This story is told in the omniscient point of view. The omniscient point of view means that the
storyteller knows all of the thoughts and emotions of all the characters throughout the story. In the
nineteenth century there was a lot of racial discrimination against African–Americans. The
protagonist is Desiree; she is formed throughout the many situations in the short story. She is fought
by the antagonist, Armand Aubigny. He is described, "as a cruel, arrogant man who, likely, would
never admit he was wrong." At the end of this story Armand's assumptions and inferences catch up
to him. "An inference is nothing more than a guess, an assumption." Inferring does not always get us
in trouble and it would almost be impossible to not assume throughout an entire day. "Unfortunately,
making some inferences can have serious consequences." There are is a lot of foreshadowing
throughout the short story that tells us that Armand has black ancestry. There is a key part of
foreshadowing, there is another form of foreshadowing when Madame Valmonde says, "the child
has grown, has changed." This is another monstrous form of foreshadowing, that there is something
different about this child and that we will find out what is different later in the story.
It had never hit Desiree that her baby was black until one day, "The baby, half naked, lay
asleep...One of La Blanche's little quadroon

Marginalia Billy Collins Summary
In the poem "Marginalia", by Billy Collins takes a look at the marginal notes written in books. The
marginal notes are seen as a communication between the reader and the words in the book. The note
sometimes can be negative because they are a way of releasing frustration with the work, especially
if not well received, but sometimes they are a way of celebrating on the work. [The narrator asserts
students to join the conversation with author's words by writing marginal notes; however, one may
say it proves to be a distraction, It causes readers to pre–judge a book and lastly, it prevents one
from understanding the text.]
Marginal notes can be viewed as distractions to new owners of passing down texts. They
are,"Sometimes the notes are ferocious,"(Collins) when the narrator refers to ferocious, This implies
that the previous owner wrote comments in discontent. When the notes symbolize discontent they
prove to be a distraction because people instead of focusing on the text will start reading the
comments therefore getting disoriented and not focusing the text. When he refers ,"If i could just get
my hands on you, Kierkegaard or Conor Cruise O'Brien" this implies he could be doing something
better than reading their works. It also means, as the new reader sees the text may see their writings
on the margin and wonder why the previous owner wrote that and may get lost or impede him from
understanding and acknowledging the text and interpret the text from the actual meaning.

Student B As A Reader
Student B as a Reader (Kaylee)
What about the student as an individual (experiences, skills, interests) provides insight into his or
her work samples and your analysis of them? In a class where most students either fall into the
categories of being high achieving or low scoring, Kaylee is one of few students in the middle. Next
year, students have the choice of going on the college track in language arts and I would like Kaylee
to feel comfortable in her abilities to choose the college track. She feels that she is not good at
reading and therefore, she needs to stay in regular language arts next year.
What are your instructional goals to promote growth for this student as a reader and interpreter of
text in a variety of media? My ... Show more content on Helpwriting.net ...
The first assignment to accomplish these goals is the Lyric Analysis Assignment. This assignment is
aligned with all four instructional goals. Students were asked to analyze a song of their choice as if
it were a poem. I used several reading strategies for differentiation in my class while teaching
analysis of poem or song. I allowed them choice of song in their assignment as a strategy to engage
the students in the reading instruction. This is a tiered assignment with two parts. The strategy of
scaffolding the assignment into two manageable parts in which the second part builds on the first
allows the students to build on their thoughts in an organized way. This is a good strategy for Kaylee
in particular so she does not get overwhelmed by the assignment as a whole. The students were to
create a key using Google Comments in Google Docs that included a minimum of four literary
devices that they found in their song. I use the Google Classroom and Google Apps for most
assignments now. Technology allows me to differentiate instruction to meet instructional goals
because I can provide immediate written feedback. This circumvents the problems of just giving oral
feedback. Kaylee will never lose the feedback and she can always revisit it. They needed to include
an inference sentence for each device and explain the effect that the singer gains by using that
device. I used the strategy of modeling for

Why Statistics Are Part Of Everyday Life
Statistics are part of everyday life. Whether we know it or not we use them in some way, shape or
form during the course of our day. Business and medical professionals, sociologists and scientists
alike all utilize statistics in order to determine probabilities of outcomes. The purpose of this paper is
to discuss what I have learned about statistics in during this class covered during the last five weeks.
The topics I will cover inform the class are descriptive statistics, inferential statistics, hypothesis
development and testing, selection of appropriate statistical tests and evaluating statistical results.
The intent is to discuss the application of the elements in analyzing and making decisions about
data. Our book states that there are many kinds of descriptive statistics. Calculating measures of
central tendency and measures of variability are two we focused on. As their names suggest,
measures of central tendency indicate what is most typical in a data set. Measures of variability
gauge how much difference there is in a set of measures (Tanner &Youseef–Morgan, 2013).
Descriptive statistics aid in sorting through large amounts of data and making them understandable.
In order to measure central tendency we utilize the mean, median, and mode. These are used to find
the most frequent value (mode), the middle most value (median), and the average (mean). These
would be useful if you wanted to use age as a data point in a marketing campaign to target the age

Reading Comprehension Strategy Of Inferring Essay
Making Inferences WHY:
Purpose of Lesson: The purpose of this lesson is to introduce students to the reading comprehension
strategy of inferring. The students will learn how to use textual evidence combined with their own
background knowledge to make inferences about various aspects of a text including characters,
setting, theme, and plot.
Common Core State Standards:
CCSS.ELA–LITERACY.RL.5.1: Quote accurately from a text when explaining what the text says
explicitly and when drawing inferences from the text.
Goals/Objectives of Lesson: What do you hope for/expect?
In a small group setting, each student will make at least 3 inferences about various scenarios and
pictures through writing and speaking.
Following a teacher's model, each student will write their own short scenario that will be used for
their classmates to make inferences about.
When reading a selected chapter from the book, Wonder, students will use a graphic organizer to
make at least two written inferences by filling in all three columns of the organizer (what is stated,
what I already know, and my inference).
Extension Activity: Each student will create a short story (either written or verbal) based on their
inferences about a picture that will be provided by the teacher.
Context of the Lesson: Where does this lesson fall in what is currently being taught?
This is the students second week of academics, and this will be their first experience in fifth grade
with making inferences. Up until

Action Research Project for Reading
PHASE 3, PART 1
A) Restatement of problem The problem stated in the Action Research Project was that 66% of third
grade students lack the ability to draw conclusions and make inferences to answer comprehension
questions correctly. This was evident by the state reading test scores, specifically the inference test
given by their teacher. This indicated a need for increased student achievement in making inferences
and drawing conclusions. Third grade students were not proficient at making inferences to gain a
deeper understanding of the texts read and thus answer open ended questions correctly. Therefore,
they did not get all the inference based comprehension questions correct based on the end of grade
test scores that these third ... Show more content on Helpwriting.net ...
Students' work was scored based on their ability to follow the directions and their ability to infer
what had happened in the story. The students used the Inferencing Rubric (Appendix C, p. 12) to
guide them as they write a narrative. The rubric was had five columns and each column had the
criteria to earn one to four points, totaling twenty points. This rubric was used to show the students
what the expectations were before they began writing their paper. The rubric was also used by the
teacher to score their final paper. Scores were tallied up and fifteen through twenty points would be
passing scores. The students were required to take an Inferencing Post Test (Appendix D, p. 13)
after they had been taught the lessons in the action research project. This is the same test they took
before the implementation. It was administered during consists of several reading passages and
multiple choice answers. There were fiction, non–fiction, and poetry selections the students read and
then answered questions based on inferences created from the passages. Students answered the
circled questions. These results were compared to the scores gathered prior to the implementation.
The End of Grade Test (Appendix E, p.21) was administered to all third grade students in Wake
County are required to take at the end of each year. This is a multiple choice test that is given at the
end of each school year. This assessment is given in a secure setting over three days. The first

Article Review : Toddlers Infer Higher Order Relational...
Article Review: Toddlers Infer Higher–Order Relational Principles in Causal Learning Tracy Van
Lone Central Washington University Article Review: Toddlers Infer Higher–Order Relational
Principles in Causal Learning There is speculation in the scientific community that human children
are born with the adaptation to "think and act like scientists and philosophers" (Blaisdell, 2015).
Previous research already told us that infants and toddlers make causal inferences that are more or
less abstract, higher–order, or relational in many ways. However, the previous studies indicate that
learning higher–order relations and using them to guide actions is dependent on direct instruction,
training, language, and/or cultural input. The objective of Caren Walker and Alison Gopnik's 2013
study entitled "Toddlers Infer Higher–Order Relational Principles in Causal Learning" was to
determine whether human toddlers have an ability to infer causal higher–order relations to solve
tasks without guidance, cues or extensive training. This article explored cognitive development
theory, and provides supporting evidence for the newly developing rational constructivism
framework ("rational constructivism"). Additionally, evolution and adaptation are explored as the
primary influencers of the framework. Previous Research Many studies, including many of this
authors' own (underlined), were cited in relation to and support of this study; studies showing– that
children as young as 16 to 24 months of

Plath Daddy Poem Meaning
The title of the poem "Daddy" immediately makes the reader think of authority due to the childish
nature of the word. This is idea is reinforced from the first line of "You do not do, you do not do",
implying that someone is scolding a child perhaps. However, it could also have sexual undertones to
it, referring to Plath's relationship to her husband which could infer there were similarities between
her father and her husband. Furthermore, this links to the idea that it could also have an underlying
theme of abuse; whether this is abuse by her father or by her husband, is unclear through the title,
but is explored throughout the poem.
Plath's father, Otto Plath, had gangrene which eventually caused his entire leg to be amputated. Plath
was severely impacted by his death as she feels as though she "lived like a foot", suggesting that her
father was negligent of Plath which made ... Show more content on Helpwriting.net ...
Plath broadens her feelings out to include all women, maybe because there are some who tolerate
domestic abuse, or because fascist leaders like Hitler became something of a sex symbol in the
states they controlled. Plath can be seen as an early feminist, so this statement can be argued to be
ironic. The internal rhyme of 'boot' and 'brute', repeated 3 times, and the consonant 'heart'; when
read aloud, this sounds as one is spitting with disgust. The last few stanzas of the poem are
ambiguous as Plath constantly repeats that she is "through". It can be inferred that Plath is putting
the past behind her and that the poem has feelings of closure towards her father, which makes the
overall tone of the poem therapeutic, which makes the poem confessional, unlike the beginning of
the poem where it appears the poem is conveying her bitterness. However, the telephone is
mentioned as a form of communication. It is unclear whether she is 'through' meaning finished with
him, or that she's finally communicated her rejection of

Taking a Look at Inductive Interference
An example of inductive inference would be the assumption that if you meet someone in public and
they are nice, and you have no reason to judge the next person, then they will be nice as well.
Inductive inferences are an unconscious way to process events in a way that supports positive
morality; without inductive inferences, people would be left to deal with only truths and although
the truth is normally a good thing, it may not be best for everyone to know. With inductive
inferences, people can live their lives making positive assumptions which put the mind at ease
versus knowing negative things that can ultimately lead to overstress.
The previous example of inductive inference relies on SPI because the inference is based on your
perception thus far of the scenario; if your perception of random people is that they are nice, then
per the SPI, you can only assume the next person will be as well because it is the only information
you have.
Based solely on experience, one cannot be justified in accepting the SPI because they would not be
making a real inference; their experiences do not necessarily entail truth in their conclusion. One
would also not be justified on the basis of deductive inference because that method means that the
conclusion is only factual to the premises it was formed from; in other words, you can only assume
everyone will be nice if there are no mean people to begin with. Lastly, assuming SPI will always
yield a correct conclusion is also not

Determining The Standard Error, Or Standard Deviation Of...
Assignment
Ans 1.1
a) The standard error, or standard deviation of the sample mean, = population standard
deviation/square root of sample size = $10.75/14
b) z = (60–68.30)/10.75 = –0.77
from the z tables we see that the probability that z is less than –0.77 is .2206, so the probability that
x is GREATER than 60 is 1–.2206 = .7794.
c) Now we are dealing with variation of the sample mean, so the standard error is 10.75/sqrt(100) =
1.075
Z = (66–68.30)/1.075 = –2.14, and the associated probability is .0162.
5. 150–138=12 = 1 standard deviation. By the empirical rule, we know that 68% of the data fall
within 1 standard deviation of the mean, above and below. Since the normal distribution is
symmetrical, 34% fall within 1 standard deviation below the mean (i.e. between 138 and 150). So a
score of 138 is the equivalent of a z score of –1. Similarly, a score of 162 is the equivalent of a z
score of 1.
By the Empirical Rule, approximately 95% of the data fall within 2 standard deviations above and
below the mean, so that would be from 126 to 174.
Same principle as above, but this time it 's plus or minus 3 standard deviations, from 114 to 186.
Ans 1.2:
a) The standard error of the mean, also called the standard deviation of the mean, is a method used
to estimate the standard deviation of a sampling distribution. To understand this, first we need to
understand why a sampling distribution is required.
In statistics, point estimation involves the use of sample data to

A & M Research Statement
Research Statement
Nilabja Guha Texas A&M University
My current research at Texas A&M University is in a broad area of uncertainty quantification (UQ),
with applications to inverse problems, transport based filtering, graphical models and online
learning. My research projects are motivated by many real–world problems in engineering and life
sciences. In my current postdoctoral position in the Institute for Scientific Computation (ISC) at
Texas A&M University, I have worked with Professor Bani K. Mallick from the department of
statistics and Professor Yalchin Efendiev from the department of mathematics. I have collaborated
with researchers in engineering and bio–sciences on developing rigorous uncertainty quantification
methods within the Bayesian ... Show more content on Helpwriting.net ...
A hierarchical Bayesian model is developed in the inverse problem setup. The Bayesian approach
contains a natural mechanism for regularization in the form of a prior distribution, and a LASSO
type prior distribution is used to strongly induce sparseness. We propose a variational type algorithm
by minimizing the Kullback–Leibler divergence between the true posterior distribution and a
separable approximation. The proposed method is illustrated on several two–dimensional linear and
nonlinear inverse problems, e.g., Cauchy problem and permeability estimation problem. The
proposed method performs comparably with full Markov chain Monte Carlo (MCMC) in terms of
accuracy and is computationally

Essay On Bayesian Analysis
2.1 Bayesian Analysis
Before making research on Bayesian analysis we need to know more about Bayes' theorem, which is
the basis of the Bayesian analysis approach. For the first of all, we need to know who the founder of
this theorem is. Thomas Bayes who is the mathematician is the person who first lodged Bayes
theorem. In his article published in 1763, Bayes introduce a version of the equation of probability
which is now known as Bayes theorem. When the first paper was published, there is little
expectation that simple equation can solve many problems in the theory of chance. But after a
hundred years later, Bayes theorem had become an important and currently as a basis for Bayesian
statistical inference. To understand the Bayes theorem, we must first understand the conditional
probability.
Bayesian analysis facilitates the use of new information to update (modify) initial probability
estimates. It is also can use historical probabilities to revise the probability estimates associated with
a new project or process. It is a powerful risk assessment and management tool. Bayesian analysis
generally requires that each component of a project or process have an associated estimated
probability (chance of happening).
It is primarily used to analyze the probabilities associated with the variables that compromise any
process or project. When ... Show more content on Helpwriting.net ...
As Beech (1990) relates, the essence of decision making is the effort to do the right thing. It has no
other purpose. The entire manager tried to come out with the right decision. Each of their
interactions is driven by a decision. With this decision, it will determine the destiny of the
management and the organization. These decisions communicate a vision that needs to be done by
the people in the management. If decision making were simple, evidence would exist of brilliantly
run organizations at all levels. It is deceptively difficult because it is risky and demanding

Advantages Of Naive Bayes Classifier
IV. NAIVE BAYES CLASSIFIERS
Naïve Bayes classifier is a simple but effective the Bayesian classifier built upon the strong
assumption that different features are independent with each other. Classification is done by
selecting the highest posterior of classification variable given a set of feature. Naive Bayes
classifiers assume that the effect of a variable value on a given class is independent of the values of
other variable. This assumption is called class conditional independence. An advantage of the naive
Bayes classifier is that it requires a small amount of training data to estimate the variable values
necessary for classification. 1. Each data sample is represented by an n dimensional feature vector,
X = (x1, x2..... xn), depicting n ... Show more content on Helpwriting.net ...
A set of cases was taken and the program was trained with these data sets such that the probabilities
of all the classes with all the conditions were calculated. Result was stored in database and when the
test data was given we got the probabilities for the various classes for the given symptom values on
the basis of which we inferred that the patient fell into the class with the highest probability. This is
what is called the Naïve Bayes‟ classification. This is a very powerful technique that is instrumental
in helping us predict the category a patient falls into.
Swine flu is presumptively diagnosed clinically by the patient's history of association with people
known to have the disease and their symptoms listed above. Usually, a quick test (for example,
nasopharyngeal swab sample) is done to see if the patient is infected with influenza A or B virus.
Most of the tests can distinguish between A and B types. The test can be negative (no flu infection)
or positive for type A and B. If the test is positive for type B, the flu is not likely to be swine flu. If it
is positive for type A, the person could have a conventional flu strain or swine flu.

Recommendation For Revision Of Penn Mart 's Health Care...
Abstract
This paper is an analysis of the critical points of the "Recommendation for Revision of Penn–Mart's
Health Care Strategy" memo (Penn–Mart Memo) in relation to the points in the report by Roy
Eichhorn, Strategic Systems Department, Army Management Staff College, entitled, Developing
Thinking Skills: Critical Thinking at the Army Management Staff College. The author of the Penn–
Mart memo is Senior VP of Human Resources, Salvador Monella whose indicated purpose is to
offer a remedy to the spiraling costs of healthcare benefits. The analysis will be broken into sections
based off of the analysis structure defined by the report by Roy Eichhorn.
Review of the Penn–Mart Memo in Accordance with the Critical Thinking at the Army Management
Staff College report.
1. Purpose, Goal, or End in View.
The purpose or goal described in the Penn–Mart memo is a program engineered to lower employee
benefit costs paid by the company. The method of attaining this outcome would be to implement a
mandatory "Get Well" employee health screening and monitoring program. The author's conclusion
to the problem of a costly employee health benefit program is to institute a multi–prong monitoring
program would include biometric screening described as "finger stick blood tests for cholesterol and
glucose; weight, height, and waist measurement; and a blood pressure reading" as well as required
health questionnaire and annual physical examinations.
2. Question at Issue or Problem to be Solved.
The

It is easy to say that species are constantly changing,...
It is easy to say that species are constantly changing, and branching off into totally new species. But
how do we know where the species originate? Phylogenies help to show us how all kinds of species
are related to each other, and why. These relationships are put into what can be called a cladogram,
which links species to common ancestors, in turn showing where, when, how, and why these
ancestors diverged to form new species. Without phylogenies, it would be extremely difficult to put
species in specific categories or relate them to one another. Along with phylogenies can come
conflict on which species should be related to one another. This conflict causes many hypotheses
and experiments, which can lead to phylogenetic retrofitting, ... Show more content on
Helpwriting.net ...
The parareptile hypothesis is taken back at least two decades. It has recently been rediscovered and
contradicted by parsimony. Bayesian inferences support this parareptile conclusion, but parsimony
concludes the idea of turtles being a sister group to pareiasaurs, which is an anapsid group, including
Eunotosaurus. To test these hypothesis, a multitude of data is compiled to observe the stability
behind the inferences made. In this article, one main experiment was discussed through the
collection and analysis of two retrofitted matrices, phylogenetic analyses, and molecular scaffolds.
In one matrice, Eunotosaurus was added to a diapsid–focused data set, while turtles were added to
an anapsid–focused data set. The diapsid sets included a broad sampling of diapsids, which placed
turtles as sisters to sauropterygians. The anapsid set, on the other hand, included a broad sampling of
anapsids, especially parareptiles. Turtles were not included in the anapsid set. When the experiment
moves on to the phylogenetic analysis, Bayesian inferences and parsimony were brought into the
mix. After these analyses, the experiment finally includes molecular scaffolding. The effect of
molecular scaffolding was to see where extant linneages interact with molecular phylogenies. The,
the Bayesian and parsimony analyses were again repeated with these backbone constraints while
everything else is indifferent. The idea

I Am A Master 's Program At The University Of British...
I began a Master's program at the University of British Columbia School of Population and Public
Health last September. This was a culmination of my desire to understand the connections between
societal issues and life sciences, and to strengthen my problem solving skills in this regard. In the
short time that I have been at the program, I had the chance to understand more about what a career
in clinical trials would entail, and to develop the focus of my research thesis at an advanced level.
My exposure to clinical research has also confirmed my passion for the field, as there are days
where I work all through the night and into the early hours of the morning, sustained by sheer
passion. In consideration of these factors namely my skills, my academic interests and natural
proclivities, I have been inspired to transfer from the Masters to the PhD program. Ultimately, I
intend to develop my skills up to the doctoral level. It therefore makes sense to take on an
opportunity to achieve this goal sooner rather than later. While the Masters program has given me
the opportunity to develop my thesis research aims and interests, the PhD program will afford me
the knowledge and hands–on experience to effectively and responsibly execute on my research
interests. My research experiences and interests till date and their impact on my choice to enroll in
this program are described in more depth below. My collective academic and research experiences
during my undergraduate and master's

The For The Future Liabilities
In general insurance, Insurers make use of data gathered previously out of experience in order to
predict the future liabilities. Such an estimate is made through the help of a "loss function" in
decision making, as well as mathematical optimization. It is a common tendency to minimize the
loss of the risk models and hence to do so are different methods applicable in today's statistics.
Frequentist expected loss, Bayesian expected loss are mostly used; with Bayesian statistics being the
increasingly common methodology in actuarial science. Insurers also make an estimate of the
expected claims that arise in the future years, and so they need to hold reserves based on the
aggregate claim amount they could face in the near future. Hence one way of doing this is by using
the aggregate claim model. Therefore, examining and comparing the different forms of loss
distribution that could be used in the aggregate risk model analysis, besides investigating about
issues surrounding the application of Bayesian statistics in such a context. Acknowledgement: I
would like to acknowledge my mentor Ms. Preeti Sahay, to have stood as a support for me
throughout the project and in providing sufficient information for the project. Introduction:
Insurance by nature is an uncertain subject. The Insured events occur at random times, particularly
in a general insurance field, thereby the amount of claims are also random. Based on the future
ability to pay the claims the insurer has to

Classification Between The Objects Is Easy Task For Humans
Classification between the objects is easy task for humans but it has proved to be a complex
problem for machines. The raise of high–capacity computers, the availability of high quality and
low–priced video cameras, and the increasing need for automatic video analysis has generated an
interest in object classification algorithms. A simple classification system consists of a camera fixed
high above the interested zone, where images are captured and consequently processed.
Classification includes image sensors, image preprocessing, object detection, object segmentation,
feature extraction and object classification. Classification system consists of database that contains
predefined patterns that compares with detected object to classify in to proper category. Image
classification is an important and challenging task in various application domains, including
biomedical imaging, biometry, video surveillance, vehicle navigation, industrial visual inspection,
robot navigation, and remote sensing. Fig. 1.1 Steps for image classification Classification process
consists of following steps a) Pre–processing– atmospheric correction, noise removal, image
transformation, main component analysis etc. b) Detection and extraction of a object– Detection
includes detection of position and other characteristics of moving object image obtained from
camera. And in extraction, from the detected object estimating the trajectory of the object in the
image plane. c) Training: Selection of the

A Machine Learning Approach For Emotions Classification
A machine learning approach for emotions classification in Micro blogs ABSTRACT Micro
blogging today has become a very popular communication tool among Internet users. Millions of
users share opinions on different aspects of life every day. Therefore micro blogging web–sites are
rich sources of data for opinion mining and sentiment analysis. Because micro blogging has
appeared relatively recently, there are a few research works that are devoted to this topic.In this
paper, we are focusing on using Twitter, which is an amazing microblogging tool and an
extraordinary communication medium for text and social web analyses.We will try to classify the
emotions in to 6 basic discrete emotional categories such as anger, disgust, fear, joy, sadness and
surprise. Keywords : Emotion Analysis; Sentiment Analysis; Opinion Mining; Text Classification 1.
INTRODUCTION Sentiment analysis or opinion mining is the computational study of opinions,
sentiments and emotions expressed in text. Sentiment analysis refers to the general method to
extract subjectivity and polarity from text.It uses a machine learning approach or a lexicon based
approach to analyse human sentiments about a topic..The challenge for sentimental analysis lies in
identifying human emotions expressed in these text. The classification of sentiment analysis goes as
follows: Machine Learning is the field of study that gives computer the ability to learn without being
explicitly programmed. Machine learning explores the

Demand & Inventory Management
Forecasting demand and inventory management using Bayesian time series
T.A. Spedding University of Greenwich, Chatham Maritime, Kent, UK K.K. Chan Nanyang
Technological University, Singapore
Batch production, Demand, Forecasting, Inventory management, Bayesian statistics, Time series
Keywords
Introduction
A typical scenario in a manufacturing company in Singapore is one in which all the strategic
decisions, including forecasting of future demand, are provided by an overseas office. The forecast
model provided by the overseas office is often inaccurate because the forecasting is performed
before the actual production schedule and it is based on marketing survey results and historical data
from an overseas research team. This ... Show more content on Helpwriting.net ...
Bayesian dynamics time series and forecasting techniques can be used to solve inventory problems
because Bayesian inference statistics has the analogue idea that posterior knowledge (actual sales
demand) can be derived from prior knowledge (such as the manager's experience) and the likelihood
(the similar or expected trend) of the product demand (Box and Tioa, 1973; Jeffreys, 1961; Lee,
1988; Press, 1989). In many real life forecasting problems (for example when previous demand data
are not available for newly launched products), there is little or no useful information
This work was carried out while the author was Associate Professor in the School of Mechanical and
Production Engineering at Nanyang Technical University in Singapore.
Integrated Manufacturing Systems 11/5 [2000] 331±339 # MCB University Press [ISSN 0957–
6061]
[ 331 ]
T.A. Spedding and K.K. Chan Forecasting demand and inventory management using Bayesian
time series Integrated Manufacturing Systems 11/5 [2000] 331±339
available at the time when the initial forecast is required. Hence, the early forecast must be based
largely on subjective considerations (such as the manager's experience and the general demand of a

similar or comparable product). As the latest information (actual sales demand) becomes available,
the forecasting model is modified with the subjective estimation in the presence of the actual data.
This

Bayesian Learning Essay examples
BAYESIAN LEARNING
Abstract
Uncertainty has presented a difficult obstacle in artificial intelligence. Bayesian learning outlines a
mathematically solid method for dealing with uncertainty based upon Bayes' Theorem. The theory
establishes a means for calculating the probability an event will occur in the future given some
evidence based upon prior occurrences of the event and the posterior probability that the evidence
will predict the event. Its use in artificial intelligence has been met with success in a number of
research areas and applications including the development of cognitive models and neural networks.
At the same time, the theory has been criticized for being philosophically unrealistic and logistically
inefficient. ... Show more content on Helpwriting.net ...
They allow intelligent systems flexibility and a logical way to update their database of knowledge.
The appeal of probability theories in AI lies in the way they express the qualitative relationship
among beliefs and can process these relationships to draw conclusions (Pearl, 1988).
One of the most formalized probabilistic theories used in AI relates to Bayes' theorem. Bayesian
methods have been used for a variety of AI applications across many disciplines including cognitive
modeling, medical diagnosis, learning causal networks, and finance.
Two years after his death, in 1763, Rev. Thomas Bayes' "Essay Toward solving a Problem in the
Doctrine of Chances" was published. Bayes is regarded as the first to use probability inductively and
established a mathematical basis for probability inference which he outlined in this now famous
paper. The idea behind Bayes' method is simple; the probability that an event will occur in future
trials can be calculated from the frequency with which it has occurred in prior trails. Let's consider
some everyday knowledge to outline Bayes' rule: where there's smoke, there's fire. We use this
everyday cliche to suggest cause and effect. But how are such relationships learned in and from
everyday experience? Conditional probability provides a way to estimate the likelihood of some
outcome given a particular situation. Bayes' theorem further refines this idea by incorporating

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