Artificial intelligence has undergone a paradigm shift since the second AI winter in the 1990s: the core of intelligence, which was reasoning, gradually replaced by learning. Since then, the field has grown remarkably with machine learning, and deep learning has become the dominant approach in the area and has been used in various algorithms and models. However, since 2016, AI researchers have been addressing the inherent limitations and flaws of the conventional approaches and trying to come up with new methods.
In this presentation, we study the history of AI. Then we discuss problems of deep learning, and then we examine Bayesian inference, which is a different approach to the dominant method of statistical learning. Then we demonstrate new ideas in AI, based on Bayesian learning and reasoning, and finally, we indicate troubles with Bayesianism.
The Agape Constant, F: A Mathematical Model of HumansPeter Anyebe
Given the natural order, and learning as the capacity to reconstruct it, it becomes possible to predict behavior from thought; then productivity and leadership would derive from self-control, which predicts learning
The Agape Constant, F: A Mathematical Model of HumansPeter Anyebe
Given the natural order, and learning as the capacity to reconstruct it, it becomes possible to predict behavior from thought; then productivity and leadership would derive from self-control, which predicts learning
In this presentation is given an introduction to Bayesian networks and basic probability theory. Graphical explanation of Bayes' theorem, random variable, conditional and joint probability. Spam classifier, medical diagnosis, fault prediction. The main software for Bayesian Networks are presented.
D. Mayo: Philosophy of Statistics & the Replication Crisis in Sciencejemille6
D. Mayo discusses various disputes-notably the replication crisis in science-in the context of her just released book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars.
For this assignment, use the aschooltest.sav dataset.The dMerrileeDelvalle969
For this assignment, use the aschooltest.sav dataset.
The dataset consists of Reading, Writing, Math, Science, and Social Studies test scores for 200 students. Demographic data include gender, race, SES, school type, and program type.
Instructions:
Work with the aschooltest.sav datafile and respond to the following questions in a few sentences. Please submit your SPSS output either in your assignment or separately.
1. Identify an Independent and Dependent Variable (of your choice) and develop a hypothesis about what you expect to find. (
note: the IV is a grouping variable, which means it needs to have more than 2 categories and the DV is continuous)
2. Run Assumption tests for Normality and initial Homogeneity of Variance. What are your results?
3. Run the one-way ANOVA with the Levene test & Tukey post hoc test.
a. What are the results of the Levene test? What does this mean?
b. What are the results of the one-way ANOVA (use notation)? What does it mean?
c. Are post hoc tests necessary? If so, what are the results of those analyses?
4. How do your analyses address your hypotheses?
Is concentration of single parent families associated with reading scores?
Using the AECF state data, the regression below measures the effect of the state's percentage of single parent families on the percentage of 4th graders with below basic reading scores.
%belowbasicread = β0 + β1x%SPF + u
Stata Output
1) Please write out the regression equation using the coefficients in the table
2) Please provide an interpretation of the coefficient for SPF
3) How does the model fit?
4) What is the NULL hypothesis for a T test about a regression coefficient?
5) What is the ALTERNATE hypothesis for a T test about a regression coefficient?
6) Look at the p value for the coefficient SPF.
a) Report the p value
b) How many stars would it get if we used our standard convention?
* p ≤ .1 ** p ≤ .05 *** p ≤ .01
image1.png
Two-Variable (Bivariate) Regression
In the last unit, we covered scatterplots and correlation. Social scientists use these as descriptive tools for getting an idea about how our variables of interest are related. But these tools only get us so far. Regression analysis is the next step. Regression is by far the most used tool in social science research.
Simple regression analysis can tell us several things:
1. Regression can estimate the relationship between x and y in their
original units of measurement. To see why this is so useful, consider the example of infant mortality and median family income. Let’s say that a policymaker is interested in knowing how much of a change in median family income is needed to significantly reduce the infant mortality rate. Correlation cannot answer this question, but regression can.
2. Regression can tell us how well the independent variable (x) explains the dependent variable (y). The measure is called the
R square.
Simple Tw ...
This talk on Bayesian statistics is tailored especially for biologists and ecologists. The main points are to highlight what the particular benefit to use Bayesian is in comparison to frequentist statistics and to understand the essence for practical application for scientific purposes.
Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Uncertain Knowledge and reasoning by Mr.Abhishek Sharma, Research Scholar from Object Automation.
A primer in Data Analysis. To substantiate the concepts, I presented Python code in the form of an ipython notebook (not included - get in touch for these, email and twitter are on the last slide).
The talk starts by describing general data analysis (and skills required). I then speak about computing descriptive statistics and explain the details of two types of predictive models (simple linear regression and naive Bayes classifiers). We build examples using both predictive models using python (Pandas and Matplotlib).
Data Science - Part XII - Ridge Regression, LASSO, and Elastic NetsDerek Kane
This lecture provides an overview of some modern regression techniques including a discussion of the bias variance tradeoff for regression errors and the topic of shrinkage estimators. This leads into an overview of ridge regression, LASSO, and elastic nets. These topics will be discussed in detail and we will go through the calibration/diagnostics and then conclude with a practical example highlighting the techniques.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
In this presentation is given an introduction to Bayesian networks and basic probability theory. Graphical explanation of Bayes' theorem, random variable, conditional and joint probability. Spam classifier, medical diagnosis, fault prediction. The main software for Bayesian Networks are presented.
D. Mayo: Philosophy of Statistics & the Replication Crisis in Sciencejemille6
D. Mayo discusses various disputes-notably the replication crisis in science-in the context of her just released book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars.
For this assignment, use the aschooltest.sav dataset.The dMerrileeDelvalle969
For this assignment, use the aschooltest.sav dataset.
The dataset consists of Reading, Writing, Math, Science, and Social Studies test scores for 200 students. Demographic data include gender, race, SES, school type, and program type.
Instructions:
Work with the aschooltest.sav datafile and respond to the following questions in a few sentences. Please submit your SPSS output either in your assignment or separately.
1. Identify an Independent and Dependent Variable (of your choice) and develop a hypothesis about what you expect to find. (
note: the IV is a grouping variable, which means it needs to have more than 2 categories and the DV is continuous)
2. Run Assumption tests for Normality and initial Homogeneity of Variance. What are your results?
3. Run the one-way ANOVA with the Levene test & Tukey post hoc test.
a. What are the results of the Levene test? What does this mean?
b. What are the results of the one-way ANOVA (use notation)? What does it mean?
c. Are post hoc tests necessary? If so, what are the results of those analyses?
4. How do your analyses address your hypotheses?
Is concentration of single parent families associated with reading scores?
Using the AECF state data, the regression below measures the effect of the state's percentage of single parent families on the percentage of 4th graders with below basic reading scores.
%belowbasicread = β0 + β1x%SPF + u
Stata Output
1) Please write out the regression equation using the coefficients in the table
2) Please provide an interpretation of the coefficient for SPF
3) How does the model fit?
4) What is the NULL hypothesis for a T test about a regression coefficient?
5) What is the ALTERNATE hypothesis for a T test about a regression coefficient?
6) Look at the p value for the coefficient SPF.
a) Report the p value
b) How many stars would it get if we used our standard convention?
* p ≤ .1 ** p ≤ .05 *** p ≤ .01
image1.png
Two-Variable (Bivariate) Regression
In the last unit, we covered scatterplots and correlation. Social scientists use these as descriptive tools for getting an idea about how our variables of interest are related. But these tools only get us so far. Regression analysis is the next step. Regression is by far the most used tool in social science research.
Simple regression analysis can tell us several things:
1. Regression can estimate the relationship between x and y in their
original units of measurement. To see why this is so useful, consider the example of infant mortality and median family income. Let’s say that a policymaker is interested in knowing how much of a change in median family income is needed to significantly reduce the infant mortality rate. Correlation cannot answer this question, but regression can.
2. Regression can tell us how well the independent variable (x) explains the dependent variable (y). The measure is called the
R square.
Simple Tw ...
This talk on Bayesian statistics is tailored especially for biologists and ecologists. The main points are to highlight what the particular benefit to use Bayesian is in comparison to frequentist statistics and to understand the essence for practical application for scientific purposes.
Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Uncertain Knowledge and reasoning by Mr.Abhishek Sharma, Research Scholar from Object Automation.
A primer in Data Analysis. To substantiate the concepts, I presented Python code in the form of an ipython notebook (not included - get in touch for these, email and twitter are on the last slide).
The talk starts by describing general data analysis (and skills required). I then speak about computing descriptive statistics and explain the details of two types of predictive models (simple linear regression and naive Bayes classifiers). We build examples using both predictive models using python (Pandas and Matplotlib).
Data Science - Part XII - Ridge Regression, LASSO, and Elastic NetsDerek Kane
This lecture provides an overview of some modern regression techniques including a discussion of the bias variance tradeoff for regression errors and the topic of shrinkage estimators. This leads into an overview of ridge regression, LASSO, and elastic nets. These topics will be discussed in detail and we will go through the calibration/diagnostics and then conclude with a practical example highlighting the techniques.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nucleic Acid-its structural and functional complexity.
Bayesian Reasoning and Learning
1. Bayesian Learning and Reasoning
Mohammad Reza Samsami
1
Sharif University of Technology
Fall 2019
2. Outline
• A brief history: from Symbolic to Connectionist AI
• Motivations
• Introduction to Bayesian Inference
• Bayesian vs Frequentist
• Bayesian method
• Point estimation
• Meaning of probability
• Bayesian linear regression
• Bayesian model comparison and averaging
• New approaches
• Troubles with Bayesianism
2
3. A brief history: from Symbolic to Connectionist AI
General Problem Solver
3
Content Technique
Back to 1959
4. A brief history: from Symbolic to Connectionist AI
4
{𝑋 ∨ 𝑌, ¬𝑌, 𝑋 → 𝑍} ⊢ 𝑍
Logic
5. A brief history: from Symbolic to Connectionist AI
5
Logic
Tree Number Pizza
6. A brief history: from Symbolic to Connectionist AI
6
Logic
Tree Number Pizza
Symbols
7. A brief history: from Symbolic to Connectionist AI
7
Tree Number Pizza
Symbols
Symbolic AI
9. A brief history: from Symbolic to Connectionist AI
9
Can machines think?
“Thinking is manipulation of symbols and Reasoning is computation.”
Thomas Hobbes
10. A brief history: from Symbolic to Connectionist AI
10
is our problem-solving procedure, and are how we
represent the world. are verbs explaining how symbols
interact with each other, or adjectives describing symbols.
Show(MohammadReza, Slides)
11. A brief history: from Symbolic to Connectionist AI
11
The set of all true things about our universe is called a knowledge
base, and we can use logic to examine our knowledge bases to answer
questions and discover new things.
The process of coming up with new propositions and checking
whether they fit with the logic of a knowledge base is called inference.
12. A brief history: from Symbolic to Connectionist AI
12
Problems with Symbolic AI
Perception
The computer itself doesn’t know what the symbols mean; which
means they are not necessarily linked to any other representations of
the world in a non-symbolic way.
13. A brief history: from Symbolic to Connectionist AI
13
Problems with Symbolic AI
Monotonicity
Reasoning based on classical deductive logic is monotonic. The new
knowledge cannot undo old knowledge.
A ⋃ ⊢ 𝑋Γ
14. A brief history: from Symbolic to Connectionist AI
14
Problems with Symbolic AI
Uncertainty
15. A brief history: from Symbolic to Connectionist AI
15
Intelligence as
Reasoning
Learning
16. A brief history: from Symbolic to Connectionist AI
16
A computer program is said to learn from experience E with respect to some
class of tasks T and performance measure P if its performance at tasks in T, as
measured by P, improves with experience E
17. A brief history: from Symbolic to Connectionist AI
17
Statistics
Optimization
26. Introduction
26
Concept Learning
f(x) = 1 if x is an example of the concept C, and otherwise f(x) = 0
The goal is to learn the indicator function f, which just defines
which elements are in the set C.
Number Game
Arithmetical concept C 𝒟 = {𝑥1, … , 𝑥 𝑛} 𝑥 belongs to C?
{1, 2, … , 100}
29. Introduction
29
How can we explain this behavior and emulate it in a machine?
The classic approach to induction is to suppose we have a hypothesis
space of concepts, ℋ, such as: odd numbers, even numbers, all numbers
between 1 and 100, powers of two, all numbers ending in 8.
The subset of ℋ that is consistent with the data 𝒟 is called the version
space. As we see more examples, the version space shrinks and we
become increasingly certain about the concept.
30. Introduction
30
Why powers of 2 and not even numbers?
The key intuition is that we want to avoid suspicious coincidences. If the
true concept was even numbers, how come we only saw numbers that
happened to be powers of two?
𝑃 𝒟 𝐶𝑒𝑣𝑒𝑛 =
1
𝑠𝑖𝑧𝑒(𝐶𝑒𝑣𝑒𝑛)
𝑁
=
1
50
4
= 1.6 × 10−7
𝑃 𝒟 𝐶𝑡𝑤𝑜 =
1
𝑠𝑖𝑧𝑒(𝐶𝑡𝑤𝑜)
𝑁
=
1
6
4
= 7.7 × 10−4
39. Interpretations of Probability
39
Frequency Interpretation:
The dominant statistical practice for many years (known as the classical or
frequentist theory) defines probability in terms of the limit of conducting infinitely
many random experiments.
So it is impossible to consider the probability of a statement such as “at least 50%
of Iranians enjoy drinking Doogh.” This statement is either true or false, so its
frequentist probability is either zero or one (but we might not know which).
40. Interpretations of Probability
40
Subjective (or Bayesian) Interpretation:
“By degree of probability, we really mean, or ought to mean, degree of belief”
According to the subjective interpretation, probabilities are degrees of
confidence, or credence, or partial beliefs of suitable agents. Thus, we really have
many interpretations of probability here— as many as there are suitable agents.
In the Bayesian interpretation, we allow probabilities instead to describe degrees
of belief in such a proposition. In this way, we can treat everything as a random
variable and use the tools of probability to carry out all inference. That is, in
subjective probability, parameters, data, and hypotheses are all treated the same.
De Morgan
43. Point Estimation
Goal: Choose a good value of θ for D
Typically the posterior mean or median is the most appropriate choice
for a real valued quantity, and the vector of posterior marginals is the
best choice for a discrete quantity.
However, the posterior mode, is the most popular choice because it
reduces to an optimization problem, for which efficient algorithms often
exist.
44. Point Estimation
44
Maximum a posteriori (MAP) estimation
𝜽 𝑴𝑨𝑷 = 𝒂𝒓𝒈𝒎𝒂𝒙 𝑷(𝜽|𝑫) = 𝒂𝒓𝒈𝒎𝒂𝒙 𝑷 𝓓 𝜽 𝑷(𝜽)
𝜽 𝜽
We have avoided computing the normalization constant 𝑃(𝐷).
45. Interpretations of Probability
45
Maximum a posteriori (MAP) estimation
Pros: Easy to compute
Interpretable
Avoids overfitting – Regularization, Shrinkage
Cons: No representation of uncertainty
Not invariant to reparameterization: 𝜏 = 𝑓 𝜃 𝜏 𝑀𝐿𝐸 = 𝑓(𝜃 𝑀𝐿𝐸)
The mode is an untypical point
60. Bayesian Model Averaging
60
Full Bayesian method would avoid model selection.
When making predictions, we should theoretically use the sum rule
to marginalize the unknown model:
But model selection is still used widely in practice.
61. Pros and Cons of Bayesian Inference
61
Pros: Directly answers the questions
Avoid overfitting
Model Selection (Occam’s Razor)
Cons: Must assume prior
Intractable integral
Limited to specific approximated distributions