Comparison Of Customization Using Human Analysis And Avior...

Comparison Of Customization Using Human Analysis And Avior...
exploitation human based mostly behavior analysis. Then the malwares are classified into malware
families Worms and Trojans. The limitation of this work is that customization using human analysis
isn't potential for today's real time traffic that is voluminous and having a range of threats.
Table 1
Comparison of malware detection techniques with focus on ransomware
Authors
Technique
Limitation
Advantages
Nolen Scaife et. al. [18]
CryptoDrop – It is a alert system which work on the concept of blocking the process which deals
with alteration of large amount of users data.
CryptoDrop is unable to determine the intent of the changes it inspects.
For example, it cannot distinguish whether the user or ransomware is encrypting a set of ... Show
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The limitation is that an attacker will adopt countermeasures to beat the system as a result of this
technique uses global image based mostly options
Scheme uses real time datasets for classification prupose.
Rieck et al. [9] projected a framework for automatic analysis of malware behavior using machine
learning. This framework collected sizable amount of malware samples and monitored their
behavior employing a sandbox environment. By embedding the determined behavior in an
exceedingly vector space, they apply the training algorithms. Clustering is used to spot the novel
categories of malware with similar behavior. Assigning unknown malware to those discovered
categories is completed by classification. supported each, bunch and classification, an progressive
approach is employed for behavior–based analysis, capable of process the behavior of thousands of
malware binaries on routine.
Anderson et al. [10] given a malware detection rule supported the analysis of graphs created from
dynamically collected instruction traces. A changed version of Ether malware analysis framework
[13] is employed to gather information. the strategy uses 2–grams to condition the transition
probabilities of a Markoff process (treated as a graph). Machinery of graph kernels is employed to
construct a similarity matrix between instances within the training set. Kernel matrix is made by
exploitation 2 distinct measures of similarity: a Gaussian kernel, that measures native

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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

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

Solving The Work Type For A Person With
ASSIGNMENT – 2 AIM : Implement Naive Bayes to predict the work type for a person with
following parameters, age: 30,Quali cation: MTech, Experience: 8. OBJECTIVE : To
understand basic concept of naive bayes classi er. To implement naive bayes classi er to
predict work type for a person with given attributes. SOFTWARE REQUIREMENTS : Linux
Operating System Java Compiler Eclipse IDE MATHEMATICAL MODEL : Consider a
following set theory notations related to a program. The mathe– matical model M for Naive Bayes
classi er is given as below, M=fS,So,A,Gg Where, S=State space.i.e All prior probabities to
calculate probability of X being a part of class `c ' So= Initial State.i.e Training set of tuple A=Set of
Actions/Operators.i.e with given dataset predicting the work type for a person with give parameters.
G=Goal state.In this case predicting accurate work type for a person. THEORY : Naive Bayes
Classi er : The Naive Bayes classi er is a simple probabilistic classi er which is based on Bayes
theorem with strong and nave independence assumptions. It is one 1 of the most basic text
classi cation techniques with various applications in email spam detection, personal email sorting,
document categorization, lan– guage detection and sentiment detection. You can use Naive Bayes
when you have limited resources in terms of CPU and Memory. Moreover when the training time is
a crucial factor, Naive Bayes comes handy since it can be trained very quickly. Let X be a data
tuple. In

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

An Example About Prostate Cancer
If we take a look at an example about prostate cancer, with the data collected by Hastie, Tibshirani,
Friedman in The Elements of Statistical Learning [2] and view the scatterplot in figure 1.1, we can
see that the dependent variable, the log of the prostate specific antigen (lpsa) has a strong positive
correlation particularly with lcavol (the log cancer volume) and lcp (the log of capsular penetration)
with weaker but still strong correlations with the other dependent variables, log prostate weight
(lweight), age, log of the amount of benign prostatic hyperplasia (lbph), and percent of Gleason
scores 4 or 5 (pgg45), but not the svi (seminal vesicle invasion) and gleason (gleason score) as these
are categorical variables [2]. Below figures 1.2 and 1.3 were fit with all variables and figures 1.4
and 1.5 were simplified by removing variables that had high p values until I felt that the model was
better improved and they were fit thereafter. When we plot the fitted values against the residuals, if
there is linearity, we should get an even spread around the line at 0. If we look at figures 1.2 and 1.4,
for which the R coding can be found in the appendix below (section 7), we can see that they both
seem to have linearity with figure 1.2 having possible outliers further away from the line and figure
1.4 having a more even spread. Taking a look at figure 1.3 we can see that there is a particularly
good fit along the middle but the tails have fairly large variation, suggesting a

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

Predictive Analytics : The Use Of Data Science For...
Essay Introduction To compete effectively in an era in which advantages are ephemeral, companies
need to move beyond historical, rear–view understandings of business performance and customer
behavior and become more proactive(tableau). Predictive Analytics is the use of data science for
audience profiling. Generic audience profiling involves determining specific characteristics of your
target audience and creating specific personas to represent each type of person within your target
audience. Predictive analytics is essentially the same process, but from a data perspective (koozai).
Predictive analytics can be used in wide areas in the industry, it's importance is not constrained to a
particular domain and ranges from marketing, telecommunication, retail, banking, etc. For example,
the telecommunication industry has noticed a high customer churn since the switching costs are slim
to none. So telecommunication companies operating in this industry are looking for new ways to
differentiate themselves from competitors in order to retain customers. By using predictive analytics
as a solution to this problem, they would be able to understand the customer needs, requirements
and retain them also allowing them to acquire new ones more effectively. With predictive analytics,
companies can predict trends, understand customers, improve business, drive strategic decision
making and predict behavior. A company named Cox Communications, the third largest cable
entertainment and broadband

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

William James 's Decision Based On Intellectual Grounds
In his lecture, The Will to Believe, William James addresses how one adopts a belief. There is a
hypothesis and an option, where you choose between two live hypotheses. An option has the
characteristics to be live or dead, forced or avoidable, and momentous or trivial. In his thesis, James
argues how our passional nature must make our decisions about our beliefs when they cannot be
certainly determined on intellectual grounds, however, this is not the case, we can always make
the decision based on intellectual grounds. One can use Bayesian probability to gain some grasp of
the situation and eventually to make a decision. In section I of James' lecture, he defines hypothesis,
giving examples of live and dead hypotheses. A hypothesis is ...anything that may be proposed to
our belief. (James, sec. 1) It is anything proposed to be believed, a claim. A hypothesis may be
living or dead, depending on the recipient. James explains the difference between live or dead with
an example of believing in Mahdi. To a person that does not know about the subject at hand, it
would be a dead hypothesis. However, if this claim was presented to someone who knew the subject
matter, it would alive as ... the hypothesis is among the mind 's possibilities. (James, sec. 1) A live
hypothesis is a claim that appears to be a real possibility for the one it is proposed to. A dead
hypothesis is a claim that does not appear to be a real possibility for the one it is proposed to.
Whether a

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

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

Text Analytics And Natural Language Processing
IV. SENTIMENT ANALYSIS A. The Sentiment analysis process i) Collection of data ii)
Preparation of the text iii) Detecting the sentiments iv) Classifying the sentiment v) Output i)
Collection of data: the first step in sentiment analysis involves collection of data from user. These
data are disorganized, expressed in different ways by using different vocabularies, slangs, context of
writing etc. Manual analysis is almost impossible. Therefore, text analytics and natural language
processing are used to extract and classify[11]. ii) Preparation of the text : This step involves
cleaning of the extracted data before analyzing it. Here non–textual and irrelevant content for the
analysis are identified and discarded iii) Detecting the sentiments: All the extracted sentences of the
views and opinions are studied. From this sentences with subjective expressions which involves
opinions, beliefs and view are retained back whereas sentences with objective communication i.e
facts, factual information are discarded iv) Classifying the sentiment: Here, subjective sentences are
classified as positive, negative, or good, bad or like, dislike[1] v) Output: The main objective of
sentiment analysis is to convert unstructured text into meaningful data. When the analysis is
finished, the text results are displayed on graphs in the form of pie chart, bar chart and line graphs.
Also time can be analyzed and can be graphically displayed constructing a sentiment time line with
the chosen

A Study Of Microbial Theory
Traditionally, the study of microbial model systems in ecology has been limited, although the advent
of molecular tools such as next generation sequencing has advanced the understanding of microbial
community patterns and processes. This has resulted in a growing focus on studying fundamental
ecological processes such as assembly and stability on microbial communities (Fierer, Ferrenberg,
Flores, et al., 2012). Because of their simplicity, microbial model systems are in contrast with the
complexity of the macro–ecological communities, allowing researchers to establish and test
fundamental ecological mechanisms relevant to macro–ecological processes (Jessup, Kassen, Forde,
et al., 2004). However, the current focus of microbial ecology is on characterizing simple
community properties such as alpha beta diversity, relative abundance, and phylogenetic or
taxonomic overlap (Baberan, Casamayor Fierer, 2011). Here, we aim to move past species
inventories and abundance data towards understanding species interactions using a network
approach, allowing us to characterize the ubiquitous building blocks of pharynx community
common to all subjects of our study. Like macro–communities, fundamental ecological processes
such as niche selection, dispersal or drift, play part in the formation and stability of the human
microbiome. By using microbial communities as model systems, characterizing their ecological
properties, assembly mechanisms and community dynamics, we can gain deeper

Web Intelligence And Its Usefulness
Abstract
In the world of Information Technology (IT), there are many areas and disciplinary of research
available and Web Intelligence (WI) is one of the new sub disciplinary of Artificial Intelligence (AI)
and Advanced IT. When AI and IT is implemented on web it defines WI. WI is used to develop web
– empowered system, Wisdom Web, Web Mining, web site automation, etc. In this paper, detail
discussion is done on Web Intelligence and its usefulness in developing intelligent web. Many
literatures are also discussed related to the Web Intelligence and at the end challenges and problems
faced during the research in the area is also mentioned. This paper will provide the pathway to the
researcher who want to perform research in the field of Web Intelligence.
Keywords – Natural Language Processing, Web Intelligence, Artificial Intelligence, Advanced
Information Technology
I. Introduction
In the era of Information Technology (IT) Web Intelligence (WI) represent new sub disciplinary for
scientific research and development that explores fundamental roles as well as practical impacts of
Intelligence. T. Y. Lin and Yan–Qing Zhang [2] have described Intelligence as a specific set of
mind capabilities which allow the individual to use the acquired knowledge efficiently and to
behave appropriately in the presence of new tasks and living conditions. With the explosive growth
of internet, wireless network, web database and wireless mobile devices implies intelligence on
web. Y.Y. Yao,

Classification Of Data Mining Techniques
Abstract
Data mining is the process of extracting hidden information from the large data set. Data mining
techniques makes easier to predict hidden patterns from the data. The most popular data mining
techniques are classification, clustering, regression, association rules, time series analysis and
summarization. Classification is a data mining task, examines the features of a newly presented
object and assigning it to one of a predefined set of classes. In this research work data mining
classification techniques are applied to disaster data set which helps to categorize the disaster data
based on the type of disaster occurred in worldwide for past 10 decade. The experimental
comparison has been conducted among Bayes classification algorithms (BayesNet and NaiveBayes)
and Rules Classification algorithms (DecisionTable and JRip). The efficiency of these algorithms is
measured by using the performance factors; classification accuracy, error rate and execution time.
This work is carried out in the WEKA data mining tool. From the experimental result, it is observed
that Rules classification algorithm, JRip has produced good classification accuracy compared to
Bayes classification algorithms. By comparing the execution time the NaiveBayes classification
algorithm required minimum time.
Keywords: Disasters, Classification, BayesNet, NaiveBayes, DecisionTable, JRip.
I Introduction
Data mining is the process of extracting hidden information from the large dataset. Data mining is

Forward Software Settlement or Else
Risk Management: Case Analysis Submission (Forward Software) 1. Introduction and problem
statement
Focus software with its Focus A–B–C is the current market leader in the spreadsheet market. Focus
Software, being the first mover with its intuitive menu system with functionality like macros had the
largest market share with only one flaw, of printing graphs. Discount Software, with its VIP
Scheduler had the same menu system to ease the user in making the transition to its software
whereas Cinco, a Forward Software product, gave the users the options of either using its own menu
system or a Focus style menu system with all the functionalities like and inbuilt graph printing
ability.
With the current legal proceedings initiated by Focus ... Show more content on Helpwriting.net ...
loss if he conducts survey ($4.64 million, includes research cost) gt; loss if he doesn't conduct the
survey ($4.5 million). * The research cost for the survey should not greater than $ 0.564 million * If
he doesn't conduct the survey he should wait for Focus –Discount trial result as the loss is less, if he
doesn't wait and tries to settle it outside. In case the Focus wins the case and files another against
Forward, it would be optimum for Forward to settle it outside court
3. Basic Tree Diagram
Please refer to the attached Excel Sheet for the Tree Diagram 4. Analysis related to hiring the
outside law firm and sensitivity of the value of information to their prediction accuracy
We have tried to find the expected final monetary value (final output, in the graph Figure 1) by
varying the cost of survey charged by the law firm keeping the accuracy constant at 0.9. Without
considering the impact of fees charged by the law research firm, the cost of survey should not be
greater than $ 0.9 million
In figure 2, we have varied the prediction accuracy of the law research firm and based on the graph
we have come to the conclusion that with associated cost of $ 0.7 million the research firm should
have accuracy greater than 0.9 to reduce the expected monetary value than $ 4.5 million 5.
Probability distribution of costs under optimal decisions and sensitivity analysis of optimal cost with
various parameters
In figure 3, we have calculated EMV for

Probability Theory and Past Due Accounts Essay
MAT540 – Quantitative Methods (Homework # 2) Section A True/False Indicate whether the
sentence or statement is true or false. __F__ 1. Two events that are independent cannot be mutually
exclusive. __F__ 2. A joint probability can have a value greater than 1. __F__ 3. The intersection of
A and Ac is the entire sample space. __T__ 4. If 50 of 250 people contacted make a donation to the
city symphony, then the relative frequency method assigns a probability of .2 to the outcome of
making a donation. __T__ 5. An automobile dealership is waiting to take delivery of nine new cars.
Today, anywhere from zero to all nine cars might be delivered. It is appropriate to use the classical
method to assign a probability of 1/10 to ... Show more content on Helpwriting.net ...
all accounts fewer than 31 or more than 60 days past due. c. all accounts from new customers and
all accounts that are from 31 to 60 days past due. d. all new customers whose accounts are between
31 and 60 days past due. __C__ 15. In the set of all past due accounts, let the event A mean the
account is between 31 and 60 days past due and the event B mean the account is that of a new
customer. The union of A and B is a. all new customers. b. all accounts fewer than 31 or more than
60 days past due. c. all accounts from new customers and all accounts that are from 31 to 60 days
past due. d. all new customers whose accounts are between 31 and 60 days past due. __D__ 16. In
the set of all past due accounts, let the event A mean the account is between 31 and 60 days past due
and the event B mean the account is that of a new customer. The intersection of A and B is a. all new
customers. b. all accounts fewer than 31 or more than 60 days past due. c. all accounts from new
customers and all accounts that are from 31 to 60 days past due. d. all new customers whose
accounts are between 31 and 60 days past due. __A__ 17. The probability of an event a. is the sum
of the probabilities of the sample points in the event. b. is the product of the probabilities of the
sample points in the event. c. is the maximum of the probabilities of the sample points in the event.
d. is the minimum of the probabilities of the sample points in the event. __C__ 18. If P

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

The Static Model Of Data Mining Essay
Abstract: Lot of research done in mining software repository. In this paper we discussed about the
static model of data mining to extract defect .Different algorithms are used to find defects like naïve
bayes algorithm, Neural Network, Decision tree. But Naïve Bayes algorithm has the best result
.Data mining approach are used to predict defects in software .We used NASA dataset namely, Data
rive. Software metrics are also used to find defects.
Keywords: Naïve Bayes algorithm, Software Metric, Solution Architecture,.
I. INTRODUCTION
According to [1], multiple algorithms are combined to show better prediction capability using votes.
Naïve Bayes algorithm gives the best result if used individual than others. The contribution of this
paper based on two reasons. Firstly, it provides a solution architecture which is based on software
repository and secondly it provides benchmarks that provide an ensemble of data mining models in
the defective module prediction problem and compare the result. Author used NASA dataset online
[2] which contain five large software projects with thousands of modules. Bohem found 80/20 rule
and about the half modules are defect free [3]. Fixing Defects in the operational phase is
considerably more expensive than doing so in the development or testing phase. Cost–escalation
factors ranges from 5:1 to 100:1 [3]. It tells defects can be fixed in operational phase not in
development and testing phase. The study of defect prediction can be classified into

Application And User Granted Permissions
2.2.4 Application–defined and user–granted permissions The sandboxing provides an absolute
secure environment for each application, while such application is not quite useful since it can only
access itself data. To make it useful, some more information has to be provided to them. In this case
the permission mechanism was developed to allow applications access to hardware devices, Internet
connectivity, data, or OS services. Applications must request permissions by defining them
explicitly in the AndroidManifest.xml file [2]. For example, an application that needs to read
incoming SMS messages should specify in this xml file: Android currently supports more than one
hundred permissions in total, which can be categorized into four types: [ ] Permission type
Description normal The default value. A lower–risk permission that do not ask for the user 's
explicit approval. dangerous A higher–risk permission that gives permit to private user data or
control over the device; needs user 's explicit approval. signature A permission that only give to
applications that are signed with the system certificate, not for normal apps. signatureOrSystem A
permission that the system grants only to applications that are in the Android system image or that
are signed with the same key as the application that declared the permission. Table 1 Android
permission categories Before Android 6.0 Marshmallow, all permissions requests are inspected at
installation, a user can choose

A Review On Thing Net Works
In numerous genuine applications, other than the input and thing content data, there may exist
relations (or systems) among the things which can likewise be useful for proposal. For instance, in
the event that we need to prescribe papers (references) to clients in Cite ULike, the reference
relations between papers are useful for suggesting papers with comparable subjects. Different case
of thing net–works can be found in hyperlinks among site pages, motion pictures coordinated by the
same executives, et cetera. In this paper, we build up a novel progressive Bayesian model, called
Relational Collaborative Topic Regression (RCTR), to join thing relations for suggestion. The
principle commitments of RCTR are laid out.
II. Foundation: In this area, we give a brief presentation about the back–ground of RCTR, including
CF based suggestion, network factorization (MF) (likewise called inactive component model) based
CF strategies and CTR. A.CF Based Recommendation Collaborative theme relapse is proposed to
prescribe records (papers) to clients via flawlessly incorporating both input framework and thing
(archive) content data into the same model, which can address the issues confronted by MF based
CF. By joining MF and inactive Dirichlet distribution (LDA), CTR accomplishes preferable
expectation execution over MF based CF with better interpretable results. In addition, with the thing
content data, CTR can anticipate input for out–of–grid things. The graphical model of CTR is

Benford's Law And Where It Came From?
Benford's Law and where it came from?
According to Oxford dictionary, Benford's law is the principle that in any large, randomly produced
set of natural numbers, such as tables of logarithms or corporate sales statistics, around 30 percent
will begin with the digit 1, 18 percent with 2, and so on, with the smallest percentage beginning
with 9. The law is applied in analyzing the validity of statistics and financial records.
Benford's law is a mathematical theory of leading digits that was discovered by American
astronomer Simon Newcomb. In 1881 he have noticed, that the pages of logarithms book beginning
with number 1 were more worn than pages dealing with higher digits. In comparison to pages
starting with 1, they looked more clean and new. He calculated that the probability that a number
has any particular non–zero first digit is:
P(d)=Log10(1+1/d)
Where: d is a number 1,2,3,4,5,6,7,8 or 9
And P is the probability.
Using that formula he concluded that all digits don't appear with equal frequency but number 1
appear as the first digit about 30 % of the time, as supposed to digit 9 that appear less than 5 % of
the time. However, he didn't provide any theoretical explanation for his phenomena he described
and it was son forgotten. In 1938, Frank Benford, a physicist, also noticed nonuniform way of digit
distribution. He attempted to test his hypothesis by collecting and analyzing his data. After having
over 20,000 observations, he noticed that numbers fell into a

Essay On Sentiment Classification
The aspect–level sentiment analysis overcomes this problem and performs the sentiment
classification taking the particular aspect into consideration. There can be a situation where the
sentiment holder may express contrasting sentiments for the same product, object, organization etc
Techniques for sentiment analysis is generally partitioned as (1) machine learning approach, (2)
lexicon–based approach and (3) combined approach (Meddhat et al., 2014a). There are two
approaches for the lexicon–based approach. First one is the dictionarybased approach and the
second one is the corpus–based approach that utilizes the factual or semantic strategy for
discovering the polarity. The dictionary–based approach is based on finding the sentiment seed ...
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Some combined rule algorithms were proposed in (Medhat et al., 2008a). Therefore, a study on
decision tree and decision rule problem is done by Quinlan (1986). Probabilistic Classifier
Probabilistic classifiers make the utilization of blend of models for classification. Every class is
considered to be a component of the mixed model. We have described various probabilistic
classifiers for sentiment analysis problem in the next subsection. 4.1.1.4.1 Naive Bayes Classifier
(NB). It is the frequently used classifier in sentiment analysis. In sentiment analysis, naive Bayes
classifier calculates the posterior probability of either positive class or negative class depending on
the sentiment words distributed over the document. The work of naïve Bayes classifier is based on
the Bag–of–word extraction of features in which the word's position is overlooked in the whole text.
This classifier uses the Bayes theorem. It calculates the probability for the sentiment word in a
document and tells whether that word belongs to the positive or negative class. The probability can
be calculated using the given formula. This assumption results in Bayesian Network. A Bayesian
network is a directed acyclic graph containing nodes and edges, where nodes denote the random
variables and the edges denote the conditional dependencies. It is a conditional exponential
classifier that takes the feature sets with label and converts them into

The Sentiment Analysis Review
Abstract– Sentiment analysis is the computational study of opinions, sentiments, subjectivity,
evaluations, attitudes, views and emotions expressed in text. Sentiment analysis is mainly used to
classify the reviews as positive or negative or neutral with respect to a query term. This is useful for
consumers who want to analyse the sentiment of products before purchase, or viewers who want to
know the public sentiment about a new released movie. Here I present the results of machine
learning algorithms for classifying the sentiment of movie reviews which uses a chi–squared feature
selection mechanism for training. I show that machine learning algorithms such as Naive Bayes and
Maximum Entropy can achieve competitive accuracy when trained using features and the publicly
available dataset. It analyse accuracy, precision and recall of machine learning classification
mechanisms with chi–squared feature selection technique and plot the relationship between number
of ... Show more content on Helpwriting.net ...
Feature Selection
The next step in the sentiment analysis is to extract and select text features. Here feature selection
technique treat the documents as group of words (Bag of Words (BOWs)) which ignores the
position of the word in the document.Here feature selection method used is Chi–square (x2).
A chi–square test also referred to as a statistical hypothesis test in which the sampling distribution of
the test statistic is a chi–square distribution when the null hypothesis is true. The chi–square test is
used to determine whether there is a significant difference between the expected frequencies and the
observed frequencies in one or more categories.
Assume n be the total number of documents in the collection, pi(w) be the conditional probability of
class i for documents which contain w, Pi be the global fraction of documents containing the class i,
and F(w) be the global fraction of documents which contain the word w. Then, the x2–statistic of the
word between word w and class i is defined[1]

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

Network Estimation : Graphical Model
3 Network estimation: graphical model
The following projects involve network estimation problems encountered in different biological
appli– cations such as gene–gene or protein–protein interaction. The main focus has been on to
develop robust, scalable network estimation methodology.
Quantile based graph estimation
Graphical models are ubiquitous tools to describe the interdependence between variables measured
si– multaneously such as large–scale gene or protein expression data. Gaussian graphical models
(GGMs) are well–established tools for probabilistic exploration of dependence structures using
precision matrices and they are generated under a multivariate normal joint distribution. However,
they suffer from several shortcomings since ... Show more content on Helpwriting.net ...
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. 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 perform well with respect to the 'best' estimator derived

An Enquiry Concerning Human Understanding, Section 10 Essay
In Hume's 1748 publication: An Enquiry Concerning Human Understanding , Section 10 is titled Of
Miracles. This section is an extended argument against the veracity of miracles. In response to
Hume, Richard Price published Four Dissertations in 1768. In Dissertation IV, The Importance of
Christianity, the Nature of Historical Evidence and Miracles, Price outlines a Bayesian argument
against Hume's conclusions that miracles cannot ever occur.
My thesis is that Price's Bayesian argument, arguably the first use of Bayes' Theorem to challenge
another published argument fails. It fails on three fronts: it mischaracterizes Hume's argument as
non–conditional; it improperly employs a Bayesian model test case of newspaper reporting; and it
does not consider the effects of the preliminary seeding of probabilities for its Bayesian model of
miracles.
1.0 Hume's Argument Against Miracles
Hume's argument is multi–faceted but most commentators (Millican, Earman) agree that the key
summary occurs in paragraph 13.
The plain consequence is (and 'tis a general maxim worthy of our attention) That no testimony is
sufficient to establish a miracle, unless the testimony be of such a kind, that its falsehood would be
more miraculous, than the fact, which it endeavours to establish... (E 10.13)
This first quote establishes a simple probability model of a miracle occurring (Miracle Happening:
MH) given a true testimony about that event (True Testimony: TT) and Hume argues that it must be
greater

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

A M Research Statement
Research Statement
Nilabja Guha Texas AM University
My current research at Texas AM 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 AM 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

Look Into Data Mining
Who is Watching, Learning, or Knows Your life?
A Look into Data Mining Today with the ever growing use of computers in the world, information
is constantly moving from one place to another. What is this information, who is it about, and who
is using it will be discussed in the following paper. The collecting, interpreting, and determination of
use of this information has come to be known as data mining. This term known as data mining has
been around only for a short time but the actual collection of data has been happening for centuries.
The following paragraph will give a brief description of this history of data collection. Data patterns
have always been around and in the 1700s a mathematician named Thomas Bayes developed a ...
Show more content on Helpwriting.net ...
By using the same data the retailers use, plus a little more, the government can use its power to help
boost the economy. The government can do this by studying the data and, if it sees fit, it can
regulate how much or how little a retailer can sell in an area. The government could also offer
incentives to a company to open a store in an area that is in need of the products that company sells.
So by using the same mined data the government can help to monitor and make improvements in
how we live. This author does not mean to lead the reader into thinking this is the only type of data
mining that is happening every day, this is just one of the most widely recognized types. There are
many types happening how it is happening, where it is happening, and how it is used depends on
how the information in compiled and interpreted. The use of Twitter, Facebook, or who is reading
what newspaper can lead to a great many compilations of data and what they mean. So the next time
you hit that buy button on your favorite shopping website, or when you post a comment or play a
game on Facebook, or even post a comment on Twitter, you should think to yourself Who is
watching, learning, or getting to know

The Effect of Savings Rate in Canada
THE EFFECT OF SAVINGS RATE IN CANADA The impact of savings rate in an economic has
become a very conflicting issue in research and among economist all over the world. This may be
due to the importance of savings generally to the economic growth and development of any nation.
However, the structure of every economy cannot be generalised by a particular economics' variation
because various countries have different social security and pension schemes, and different tax
systems, all of which have an effect on disposable income. In addition, the age of a country's
population, the availability and ease of credit, the overall wealth, and cultural and social factors
within a country all affect savings rates within a particular country. Therefore, ... Show more content
on Helpwriting.net ...
All variables used in the study have been seasonally adjusted. For the period 1983 to 2010, table 1
below shows that SAV, PCI and DR had average values of .20366, 35.4638 and 5.4539 respectively
and also had corresponding standard deviations of .024869, 6.4639 and 3.8434. SAV, which had the
lowest mean and deviation from mean, also had a coefficient of variation of .094204 while PCI and
DR had coefficient of variation of .14290 and .76027 respectively. The high coefficient of variation
of DR implies that there is greater dispersion in the variable than in SAV which has the least
dispersion. Table 1: Statistical Summary Sample period :1983Q1 to 2010Q4 Variable(s) SAV PCI
DR Mean .20366 35.4638 5.4639 Standard Deviation .024869 6.4639 3.8434 Coefficient of
Variation .094204 .14290 .76027 As shown in table 2 below, the correlations between the variables
show that both PCI and DR were positively correlated with SAV. While PCI had a higher correlation
with a value of .34810, DR had a lower correlation with a value of .12820. This correlation indicates
a predictive positive relationship between the variables. It was also observed that RCPY and DR
were negatively correlated with a value of –.86320. Table 2: Estimated Correlation Matrix of

Statistics : Statistical Concepts For Research
Final Paper
Tamara D. McQueen
MAT 540: Statistical Concepts for Research
Dr. Veliota Drakopoulou
November 20, 2016
Final Paper
This paper will give an overview of various approaches that statistics are used in everyday life when
finances are concerned. The following three methods will be discussed: Sample Units, Probability,
and Bayes Theorem. Hopefully, we have a broader knowledge of the three methods and understand
how statistics can help in our everyday life. Let us beginning by discussing the tem statistics. The
term statistics, originated from the Latin word status, meaning state. (Johnson and
Bhattacharyya, 2014). For many when the term statistics is mentioned, one may tend to think of
numbers that compare how something was viewed by another set of persons or things compared to
another set of persons or things. However, statistics is so much more than that. Statistics help
provide a solid basis for improving the learning process. (Johnson and Bhattacharyya, 2014).
Statistics are used in our everyday lives for things like polls for employment rate, the Gallup poll,
teenage pregnancy rate, high school dropout rates, etc. The list of statistics goes on and on. Many
financial institutions use statistics to help them figured out how well or bad their company may be
doing. For, instance 1st Franklin Financial Corporation (1FFC), use statistics on a daily basis to help
their company to know they did or did not do and how or where they need to improve.

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

Dynamic News Classification Using Machine Learning
Dynamic News Classification using Machine Learning Introduction Why this classification is
needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge
amount of data would not be able to be managed easily. Text Classification is done through Text
Mining study which would help sorting the important texts from the content or a document to
manage the data or information easily. //Give a scenario, where classification would be mandatory.
Advantages of classification of news articles (Ayush) Data classification is all about tagging the data
so that it can be found quickly and efficiently.The amount of disorder data is increasing at an
exponential rate, so if we can build a machine model which can automatically classify data then we
can save time and huge amount of human resources. What you have done in this paper (all) Related
work In this paper [1] , the author has classified online news article using Term Frequency–Inverse
Document Frequency (TF–IDF) algorithm.12,000 articles were gathered 53 persons were to
manually group the articles on its topics. Computer took 151 hours to implement the whole
procedure completely and it was done using Java Programming Language.The accuracy of this
classifier was 98.3 % . The disadvantages of using this classifier was it took a lot of time due to
large number of words in the dictionary. Sometimes the text contained a lot of words that described
another category since the

The Rationality of Probabilities for Actions in Decision...
The Rationality of Probabilities for Actions in Decision Theory
ABSTRACT: Spohn's decision model, an advancement of Fishburn's theory, is valuable for making
explicit the principle used also by other thinkers that 'any adequate quantitative decision model must
not explicitly or implicitly contain any subjective probabilities for acts.' This principle is not used in
the decision theories of Jeffrey or of Luce and Krantz. According to Spohn, this principle is
important because it has effects on the term of action, on Newcomb's problem, and on the theory of
causality and the freedom of the will. On the one hand, I will argue against Spohn with Jeffrey that
the principle has to be given up. On the other, I will try to argue against ... Show more content on
Helpwriting.net ...
In 1969 Robert Nozick introduced Newcomb's problem to the philosophic community as a conflict
between the principle of expected utility maximization and the principle of dominance. Nozick's
introduction led to a Newcombmania (Levi 1982), because philosophers have decisively different
opinions about the correct solution to this problem. The Newcombmania showed itself in the
development of causal and evidential decision theories and other proposals. Because the evidential
theories (for example Jeffrey 1965, 1983) do not use the principle, they cannot give a solution to
Newcomb's problem in case you accept the principle. The causal theories which use subjunctive
conditionals (for example Lewis 1981) are problematical, because they still have to provide a logic
of subjunctive conditionals, a probability theory for subjunctive conditionals and a corresponding
decision theory. Because Skyrms' (1980) causal theory and Kyburg's (1980) proposal of epistemic
vs. stochastic independence also don't use the principle, only Spohn's solution (1978) to Newcomb's
problem is left. This solution which recommends taking both boxes is valuable for its simplicity in
contrast to the theories with subjunctive conditionals. According to Spohn it is a mistake to use
probabilities conditionalized on actions for the results of the prediction, if the prediction is earlier
than the choice. According to Spohn it is right that the

Fusion Techniques For Reliable Information
REPORT ON Fusion Techniques for Reliable Information: A Survey Hyun Lee, Byoungyong Lee,
Kyungseo Park and Ramez Elmasri Submitted by– STUDENT NAME:– Lokesh Paduchuri
STUDENT ID:– 1001049649 SUBMISSION DATE:– 04/16/2014 ABSTRACT: This report focuses
on the Data combined by multi–sensors as a critical variable for acquiring solid context oriented
data in keen spaces which utilize the pervasive and omnipresent registering strategies. Versatile
combination enhances hearty operational framework exhibitions then settles on a solid choice by
diminishing indeterminate data. Then again, these combination systems experience the ill effects of
issues with respect to the exactness of estimation or derivation. There are no regularly
acknowledged methodologies exist presently. In this report, the points of interest and detriments of
combination procedures which might be utilized as a part of particular requisitions are presented.
Secondly, well–known models, calculations, frameworks, and requisitions relying upon the
proposed methodologies are classified. At long last, the related issues for combination methods
inside the shrewd spaces then recommend research headings for enhancing the choice making in
unverifiable circumstance are recommended. INDEX 1. Introduction 2. Concept 3. Models 4.
Algorithms and Theories 5. Systems and Applications 6. Issues and Research directions 7.
Conclusion 8. References 1. INTRODUCTION: In pervasive and

Models For Diffusion Of Innovations Among Potential Adopters
Models for diffusion of innovations among potential adopters have been recently used to study the
life cycle of new products and to forecast first–purchase sales. Those models are useful for
managers as decision aids to create and perform strategies to maintain the profitability of new
products across their life cycle. Bass (1969) pioneered this area of research with a model for
diffusions of new products under peer pressure via word–of–mouth. This model distinguished two
parameters: innovation and imitation. Later, Chatterjee and Eliashberg (1990) provided a
microeconomic version of Bass's model that included interactions among potential adopters and the
formation of beliefs.
In Chatterjee and Eliashberg's model, potential adopters were risk averse and used the price and
their perceptions about the innovation's performance as inputs for utility functions. Thus, with
Bayesian methods, potential adopters updated parameters with information from past adopters. Our
model also focuses on informational influence on adoption of new products. However, we modified
Chatterjee and Eliashberg's model of beliefs formation and individual choice by taking into account
the possibility that influences take place only among consumers who are connected in a social
network.
The objective of this article is twofold. First, we seek to determine how global parameters of the
social network, such as average path length and clustering, affect diffusion processes. Second, we
attempt to identify early

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

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