DOCSLIB.ORG

The Bayesian Perspective in the Context of Large Scale Assessments

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Bayesian Perspective in the Context of Large Scale Assessments The Bayesian Perspective in the Context of Large Scale Assessments David Kaplan Department of Educational Psychology 17 February 2012 Introduction ❖ Introduction ● 1. Introduction to The research reported in this paper was supported by the Institute of Bayesian Inference Education Sciences, U.S. Department of Education, through Grant 2. An Example: R305D110001 to The University of Wisconsin - Madison. The Multilevel SEM applied to PISA opinions expressed are those of the authors and do not represent 3. Discussion views of the Institute or the U.S. Department of Education. Indiana University - WIM Symposium 2 / 55 ❖ Introduction Probability does not exist. 1. Introduction to Bayesian Inference 2. An Example: - Bruno de Finetti Multilevel SEM applied to PISA 3. Discussion Indiana University - WIM Symposium 3 / 55 ❖ Introduction ● 1. Introduction to Bruno de Finetti was an Italian probabilist and one of the most Bayesian Inference important contributors to the subjectivist Bayesian movement. 2. An Example: Multilevel SEM applied to PISA ● 3. Discussion His statement can be considered the foundational view of the subjectivist branch of Bayesian statistics. ● My goal is to argue for a subjectivist Bayesian approach to conducting research using data from large scale assessments (LSAs). Indiana University - WIM Symposium 4 / 55 ❖ Introduction OUTLINE 1. Introduction to Bayesian Inference 2. An Example: Multilevel SEM applied 1. Introduction to Bayesian inference to PISA 3. Discussion 2. An Example: Multilevel SEM applied to PISA 3. Discussion Indiana University - WIM Symposium 5 / 55 ❖ Introduction 1. Introduction to Bayesian Inference ❖ Frequentist v. Bayesian Probability ❖ Bayes’ Theorem ❖ The Prior Distribution ❖ Exchangeability ❖ Where do priors come from? ❖ The Likelihood ❖ The Posterior Distribution 1. Introduction to Bayesian Inference ❖ Bayesian Model Evaluation and Testing ❖ Implications for ILSAs 2. An Example: Multilevel SEM applied to PISA 3. Discussion Indiana University - WIM Symposium 6 / 55 Frequentist v. Bayesian Probability ❖ Introduction ● 1. Introduction to For frequentists, the basic idea is that probability is represented by Bayesian Inference the model of long run frequency. ❖ Frequentist v. Bayesian Probability ❖ Bayes’ Theorem ❖ The Prior Distribution ● Frequentist probability underlies the Fisher and Neyman-Pearson ❖ Exchangeability ❖ Where do priors come schools of statistics – the conventional methods of statistics we most from? often use. ❖ The Likelihood ❖ The Posterior Distribution ❖ Bayesian Model ● Evaluation and Testing The frequentist formulation rests on the idea of equally probable and ❖ Implications for ILSAs stochastically independent events 2. An Example: Multilevel SEM applied to PISA ● The physical representation is the coin toss, which relates to the idea 3. Discussion of a very large (actually infinite) number of repeated experiments. Indiana University - WIM Symposium 7 / 55 ❖ Introduction ● 1. Introduction to The entire structure of Neyman - Pearson hypothesis testing is Bayesian Inference based on frequentist probability. ❖ Frequentist v. Bayesian Probability ❖ Bayes’ Theorem ❖ The Prior Distribution ● Our conclusions regarding the null and alternative hypotheses ❖ Exchangeability ❖ Where do priors come presuppose the idea that we could conduct the same experiment an from? infinite number of times. ❖ The Likelihood ❖ The Posterior Distribution ❖ Bayesian Model ● Evaluation and Testing Our interpretation of confidence intervals also assumes a fixed ❖ Implications for ILSAs parameter and CIs that vary over an infinitely large number of 2. An Example: identical experiments. Multilevel SEM applied to PISA 3. Discussion Indiana University - WIM Symposium 8 / 55 ❖ Introduction ● 1. Introduction to But there is another view of probability as subjective belief. Bayesian Inference ❖ Frequentist v. Bayesian Probability ● The physical model in this case is that of the “bet”. ❖ Bayes’ Theorem ❖ The Prior Distribution ❖ Exchangeability ● ❖ Where do priors come Consider the situation of betting on who will win the World Series. from? ❖ The Likelihood ❖ The Posterior ● Distribution Here, probability is not based on some notion of an infinite number ❖ Bayesian Model of repeatable and stochastically independent events, but rather Evaluation and Testing ❖ Implications for ILSAs some notion of how much knowledge you have and how much you 2. An Example: are willing to bet. Multilevel SEM applied to PISA 3. Discussion ● Thus, subjective probability allows one to address questions such as “what is the probability that the Cubs will win the World Series?” Relative frequency supplies information, but it is not the same as probability and can be quite different. ● This notion of subjective probability underlies Bayesian statistics. Indiana University - WIM Symposium 9 / 55 Bayes' Theorem ❖ Introduction ● 1. Introduction to Consider the joint probability of two events, Y and X, for example Bayesian Inference observing smoking and lung cancer jointly. ❖ Frequentist v. Bayesian Probability ❖ Bayes’ Theorem ❖ The Prior Distribution ● The joint probability can be written as ❖ Exchangeability ❖ Where do priors come from? p(Y,X) = p(Y |X)p(X) (1) ❖ The Likelihood ❖ The Posterior Distribution ❖ Bayesian Model Evaluation and Testing ● Similarly ❖ Implications for ILSAs 2. An Example: Multilevel SEM applied p(X, Y ) = p(X|Y )p(Y ) (2) to PISA 3. Discussion Indiana University - WIM Symposium 10 / 55 ❖ Introduction ● 1. Introduction to Because these are symmetric, we can set them equal to each other Bayesian Inference to obtain the following ❖ Frequentist v. Bayesian Probability ❖ Bayes’ Theorem p(Y |X)p(X) = p(X|Y )p(Y ) (3) ❖ The Prior Distribution ❖ Exchangeability Therefore ❖ Where do priors come from? p(X|Y )p(Y ) ❖ The Likelihood p(Y |X) = (4) ❖ The Posterior p(X) Distribution ❖ Bayesian Model Evaluation and Testing and the inverse probability theorem (Bayes’ theorem) states ❖ Implications for ILSAs 2. An Example: p(Y |X)p(X) Multilevel SEM applied p(X|Y ) = (5) to PISA p(Y ) 3. Discussion ● Why do we care? Because this is how you could go from the probability of having cancer given that the patient smokes, to the probability that the patient smokes given that he/she has cancer. ● We simply need the marginal probability of smoking and the marginal probability of cancer (what we will call prior probabilities). Indiana University - WIM Symposium 11 / 55 ❖ Introduction ● 1. Introduction to A fundamental difference (among many) between Bayesians and Bayesian Inference frequentists, concerns the nature of parameters. ❖ Frequentist v. Bayesian Probability ❖ Bayes’ Theorem ● ❖ The Prior Distribution Frequentists view parameters as unknown fixed characteristics of a ❖ Exchangeability population, estimated from sample data. ❖ Where do priors come from? ❖ The Likelihood ❖ The Posterior ● Bayesians view parameters as unknown but random quantities that Distribution ❖ Bayesian Model can be characterized by a distribution. Evaluation and Testing ❖ Implications for ILSAs ● 2. An Example: In fact, Bayesians view all unknowns as possessing probability Multilevel SEM applied to PISA distributions. 3. Discussion ● This is a fundamental difference! It has implications for estimation. Indiana University - WIM Symposium 12 / 55 ❖ Introduction ● 1. Introduction to Because Bayesians view parameters probabilistically, we need to Bayesian Inference assign them probability distributions. ❖ Frequentist v. Bayesian Probability ❖ Bayes’ Theorem ● Consider again Bayes’ theorem in light of data and parameters. ❖ The Prior Distribution Since everything is random possessing a probability distribution, we ❖ Exchangeability ❖ Where do priors come can write Bayes’s theorem as from? ❖ The Likelihood p(Y |θ)p(θ) ❖ The Posterior p(θ|Y ) = (6) Distribution p(Y ) ❖ Bayesian Model Evaluation and Testing ❖ Implications for ILSAs where p(θ|Y ) is the posterior distribution of θ given the data Y , p(θ) 2. An Example: is the prior distribution of θ, and p(Y ) is the marginal distribution of Multilevel SEM applied the data. to PISA 3. Discussion ● Note that the marginal distribution p(Y ) does not involve model parameters. It is there to normalize the probability so that it integrates to 1.0. As such, we can ignore it and write Bayes’ theorem as p(θ|Y ) ∝ p(Y |θ)p(θ) (7) Indiana University - WIM Symposium 13 / 55 ❖ Introduction ● 1. Introduction to Let’s look at Bayes’ theorem again. Bayesian Inference ❖ Frequentist v. p(θ|Y ) ∝ p(Y |θ)p(θ) (8) Bayesian Probability ❖ Bayes’ Theorem ❖ The Prior Distribution ❖ Exchangeability ❖ Where do priors come ● This simple expression reflects a view about the evolutionary from? ❖ The Likelihood development of knowledge. It says ❖ The Posterior Distribution ❖ Bayesian Model Current knowledge ∝ Current evidence/data × Prior knowledge Evaluation and Testing ❖ Implications for ILSAs 2. An Example: ● The problem lies in what we consider to be prior knowledge. Multilevel SEM applied to PISA ● Frequency is not enough. We use frequency as a piece of 3. Discussion information to inform a subjective experience of uncertainty which we quantify as probability. ● Probability does not exist as a feature of the external world. It is a subjective quantification of uncertainty. This is the essence of de Finetti’s quote. Indiana University - WIM Symposium 14 / 55 The Prior Distribution ❖ Introduction ● 1. Introduction to A critically important part of Bayesian statistics is the prior Bayesian Inference distribution. ❖ Frequentist v. Bayesian Probability
Load more

Recommended publications
  • Statistical Modelling
    Statistical Modelling Dave Woods and Antony Overstall (Chapters 1–2 closely based on original notes by Anthony Davison and Jon Forster) c 2016 StatisticalModelling ............................... ......................... 0 1. Model Selection 1 Overview............................................ .................. 2 Basic Ideas 3 Whymodel?.......................................... .................. 4 Criteriaformodelselection............................ ...................... 5 Motivation......................................... .................... 6 Setting ............................................ ................... 9 Logisticregression................................... .................... 10 Nodalinvolvement................................... .................... 11 Loglikelihood...................................... .................... 14 Wrongmodel.......................................... ................ 15 Out-of-sampleprediction ............................. ..................... 17 Informationcriteria................................... ................... 18 Nodalinvolvement................................... .................... 20 Theoreticalaspects.................................. .................... 21 PropertiesofAIC,NIC,BIC............................... ................. 22 Linear Model 23 Variableselection ................................... .................... 24 Stepwisemethods.................................... ................... 25 Nuclearpowerstationdata............................ ....................
    [Show full text]
  • Introduction to Bayesian Estimation
    Introduction to Bayesian Estimation March 2, 2016 The Plan 1. What is Bayesian statistics? How is it different from frequentist methods? 2. 4 Bayesian Principles 3. Overview of main concepts in Bayesian analysis Main reading: Ch.1 in Gary Koop's Bayesian Econometrics Additional material: Christian P. Robert's The Bayesian Choice: From decision theoretic foundations to Computational Implementation Gelman, Carlin, Stern, Dunson, Wehtari and Rubin's Bayesian Data Analysis Probability and statistics; What's the difference? Probability is a branch of mathematics I There is little disagreement about whether the theorems follow from the axioms Statistics is an inversion problem: What is a good probabilistic description of the world, given the observed outcomes? I There is some disagreement about how we interpret data/observations and how we make inference about unobservable parameters Why probabilistic models? Is the world characterized by randomness? I Is the weather random? I Is a coin flip random? I ECB interest rates? It is difficult to say with certainty whether something is \truly" random. Two schools of statistics What is the meaning of probability, randomness and uncertainty? Two main schools of thought: I The classical (or frequentist) view is that probability corresponds to the frequency of occurrence in repeated experiments I The Bayesian view is that probabilities are statements about our state of knowledge, i.e. a subjective view. The difference has implications for how we interpret estimated statistical models and there is no general
    [Show full text]
  • Hybrid of Naive Bayes and Gaussian Naive Bayes for Classification: a Map Reduce Approach
    International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue-6S3, April 2019 Hybrid of Naive Bayes and Gaussian Naive Bayes for Classification: A Map Reduce Approach Shikha Agarwal, Balmukumd Jha, Tisu Kumar, Manish Kumar, Prabhat Ranjan Abstract: Naive Bayes classifier is well known machine model could be used without accepting Bayesian probability learning algorithm which has shown virtues in many fields. In or using any Bayesian methods. Despite their naive design this work big data analysis platforms like Hadoop distributed and simple assumptions, Naive Bayes classifiers have computing and map reduce programming is used with Naive worked quite well in many complex situations. An analysis Bayes and Gaussian Naive Bayes for classification. Naive Bayes is manily popular for classification of discrete data sets while of the Bayesian classification problem showed that there are Gaussian is used to classify data that has continuous attributes. sound theoretical reasons behind the apparently implausible Experimental results show that Hybrid of Naive Bayes and efficacy of types of classifiers[5]. A comprehensive Gaussian Naive Bayes MapReduce model shows the better comparison with other classification algorithms in 2006 performance in terms of classification accuracy on adult data set showed that Bayes classification is outperformed by other which has many continuous attributes. approaches, such as boosted trees or random forests [6]. An Index Terms: Naive Bayes, Gaussian Naive Bayes, Map Reduce, Classification. advantage of Naive Bayes is that it only requires a small number of training data to estimate the parameters necessary I. INTRODUCTION for classification. The fundamental property of Naive Bayes is that it works on discrete value.
    [Show full text]
  • Adjusted Probability Naive Bayesian Induction
    Adjusted Probability Naive Bayesian Induction 1 2 Geo rey I. Webb and Michael J. Pazzani 1 Scho ol of Computing and Mathematics Deakin University Geelong, Vic, 3217, Australia. 2 Department of Information and Computer Science University of California, Irvine Irvine, Ca, 92717, USA. Abstract. NaiveBayesian classi ers utilise a simple mathematical mo del for induction. While it is known that the assumptions on which this mo del is based are frequently violated, the predictive accuracy obtained in discriminate classi cation tasks is surprisingly comp etitive in compar- ison to more complex induction techniques. Adjusted probability naive Bayesian induction adds a simple extension to the naiveBayesian clas- si er. A numeric weight is inferred for each class. During discriminate classi cation, the naiveBayesian probability of a class is multiplied by its weight to obtain an adjusted value. The use of this adjusted value in place of the naiveBayesian probabilityisshown to signi cantly improve predictive accuracy. 1 Intro duction The naiveBayesian classi er Duda & Hart, 1973 provides a simple approachto discriminate classi cation learning that has demonstrated comp etitive predictive accuracy on a range of learning tasks Clark & Niblett, 1989; Langley,P., Iba, W., & Thompson, 1992. The naiveBayesian classi er is also attractive as it has an explicit and sound theoretical basis which guarantees optimal induction given a set of explicit assumptions. There is a drawback, however, in that it is known that some of these assumptions will b e violated in many induction scenarios. In particular, one key assumption that is frequently violated is that the attributes are indep endent with resp ect to the class variable.
    [Show full text]
  • The Bayesian New Statistics: Hypothesis Testing, Estimation, Meta-Analysis, and Power Analysis from a Bayesian Perspective
    Published online: 2017-Feb-08 Corrected proofs submitted: 2017-Jan-16 Accepted: 2016-Dec-16 Published version at http://dx.doi.org/10.3758/s13423-016-1221-4 Revision 2 submitted: 2016-Nov-15 View only at http://rdcu.be/o6hd Editor action 2: 2016-Oct-12 In Press, Psychonomic Bulletin & Review. Revision 1 submitted: 2016-Apr-16 Version of November 15, 2016. Editor action 1: 2015-Aug-23 Initial submission: 2015-May-13 The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective John K. Kruschke and Torrin M. Liddell Indiana University, Bloomington, USA In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty, on the other hand. Among frequentists in psychology a shift of emphasis from hypothesis testing to estimation has been dubbed “the New Statistics” (Cumming, 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis. Keywords: Null hypothesis significance testing, Bayesian inference, Bayes factor, confidence interval, credible interval, highest density interval, region of practical equivalence, meta-analysis, power analysis, effect size, randomized controlled trial. he New Statistics emphasizes a shift of empha- phasis. Bayesian methods provide a coherent framework for sis away from null hypothesis significance testing hypothesis testing, so when null hypothesis testing is the crux T (NHST) to “estimation based on effect sizes, confi- of the research then Bayesian null hypothesis testing should dence intervals, and meta-analysis” (Cumming, 2014, p.
    [Show full text]
  • Applied Bayesian Inference
    Applied Bayesian Inference Prof. Dr. Renate Meyer1;2 1Institute for Stochastics, Karlsruhe Institute of Technology, Germany 2Department of Statistics, University of Auckland, New Zealand KIT, Winter Semester 2010/2011 Prof. Dr. Renate Meyer Applied Bayesian Inference 1 Prof. Dr. Renate Meyer Applied Bayesian Inference 2 1 Introduction 1.1 Course Overview 1 Introduction 1.1 Course Overview Overview: Applied Bayesian Inference A Overview: Applied Bayesian Inference B I Conjugate examples: Poisson, Normal, Exponential Family I Bayes theorem, discrete – continuous I Specification of prior distributions I Conjugate examples: Binomial, Exponential I Likelihood Principle I Introduction to R I Multivariate and hierarchical models I Simulation-based posterior computation I Techniques for posterior computation I Introduction to WinBUGS I Normal approximation I Regression, ANOVA, GLM, hierarchical models, survival analysis, state-space models for time series, copulas I Non-iterative Simulation I Markov Chain Monte Carlo I Basic model checking with WinBUGS I Bayes Factors, model checking and determination I Convergence diagnostics with CODA I Decision-theoretic foundations of Bayesian inference Prof. Dr. Renate Meyer Applied Bayesian Inference 3 Prof. Dr. Renate Meyer Applied Bayesian Inference 4 1 Introduction 1.1 Course Overview 1 Introduction 1.2 Why Bayesian Inference? Computing Why Bayesian Inference? Or: What is wrong with standard statistical inference? I R – mostly covered in class The two mainstays of standard/classical statistical inference are I WinBUGS – completely covered in class I confidence intervals and I Other – at your own risk I hypothesis tests. Anything wrong with them? Prof. Dr. Renate Meyer Applied Bayesian Inference 5 Prof. Dr. Renate Meyer Applied Bayesian Inference 6 1 Introduction 1.2 Why Bayesian Inference? 1 Introduction 1.2 Why Bayesian Inference? Example: Newcomb’s Speed of Light Newcomb’s Speed of Light: CI Example 1.1 Let us assume that the individual measurements 2 2 Light travels fast, but it is not transmitted instantaneously.
    [Show full text]
  • Iterative Bayes Jo˜Ao Gama∗ LIACC, FEP-University of Porto, Rua Campo Alegre 823, 4150 Porto, Portugal
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science 292 (2003) 417–430 www.elsevier.com/locate/tcs Iterative Bayes Jo˜ao Gama∗ LIACC, FEP-University of Porto, Rua Campo Alegre 823, 4150 Porto, Portugal Abstract Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The Iterative Bayes begins with the distribution tables built by the naive Bayes. Those tables are iteratively updated in order to improve the probability class distribution associated with each training example. In this paper we argue that Iterative Bayes minimizes a quadratic loss function instead of the 0–1 loss function that usually applies to classiÿcation problems. Experimental evaluation of Iterative Bayes on 27 benchmark data sets shows consistent gains in accuracy. An interesting side e3ect of our algorithm is that it shows to be robust to attribute dependencies. c 2002 Elsevier Science B.V. All rights reserved. Keywords: Naive Bayes; Iterative optimization; Supervised machine learning 1. Introduction Pattern recognition literature [5] and machine learning [17] present several approaches to the learning problem. Most of them are in a probabilistic setting. Suppose that P(Ci|˜x) denotes the probability that example ˜x belongs to class i. The zero-one loss is minimized if, and only if, ˜x is assigned to the class Ck for which P(Ck |˜x) is maximum [5]. Formally, the class attached to example ˜x is given by the expression argmax P(Ci|˜x): (1) i Any function that computes the conditional probabilities P(Ci|˜x) is referred to as discriminant function.
    [Show full text]
  • Bayesian Analysis 1 Introduction
    Bayesian analysis Class Notes Manuel Arellano March 8, 2016 1 Introduction Bayesian methods have traditionally had limited influence in empirical economics, but they have become increasingly important with the popularization of computer-intensive stochastic simulation algorithms in the 1990s. This is particularly so in macroeconomics, where applications of Bayesian inference include vector autoregressions (VARs) and dynamic stochastic general equilibrium (DSGE) models. Bayesian approaches are also attractive in models with many parameters, such as panel data models with individual heterogeneity and flexible nonlinear regression models. Examples include discrete choice models of consumer demand in the fields of industrial organization and marketing. An empirical study uses data to learn about quantities of interest (parameters). A likelihood function or some of its features specify the information in the data about those quantities. Such specification typically involves the use of a priori information in the form of parametric or functional restrictions. In the Bayesian approach to inference, one not only assigns a probability measure to the sample space but also to the parameter space. Specifying a probability distribution over potential parameter values is the conventional way of modelling uncertainty in decision-making, and offers a systematic way of incorporating uncertain prior information into statistical procedures. Outline The following section introduces the Bayesian way of combining a prior distribution with the likelihood of the data to generate point and interval estimates. This is followed by some comments on the specification of prior distributions. Next we turn to discuss asymptotic approximations; the main result is that in regular cases there is a large-sample equivalence between Bayesian probability statements and frequentist confidence statements.
    [Show full text]
  • A Brief Overview of Probability Theory in Data Science by Geert
    A brief overview of probability theory in data science Geert Verdoolaege 1Department of Applied Physics, Ghent University, Ghent, Belgium 2Laboratory for Plasma Physics, Royal Military Academy (LPP–ERM/KMS), Brussels, Belgium Tutorial 3rd IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis, 27-05-2019 Overview 1 Origins of probability 2 Frequentist methods and statistics 3 Principles of Bayesian probability theory 4 Monte Carlo computational methods 5 Applications Classification Regression analysis 6 Conclusions and references 2 Overview 1 Origins of probability 2 Frequentist methods and statistics 3 Principles of Bayesian probability theory 4 Monte Carlo computational methods 5 Applications Classification Regression analysis 6 Conclusions and references 3 Early history of probability Earliest traces in Western civilization: Jewish writings, Aristotle Notion of probability in law, based on evidence Usage in finance Usage and demonstration in gambling 4 Middle Ages World is knowable but uncertainty due to human ignorance William of Ockham: Ockham’s razor Probabilis: a supposedly ‘provable’ opinion Counting of authorities Later: degree of truth, a scale Quantification: Law, faith ! Bayesian notion Gaming ! frequentist notion 5 Quantification 17th century: Pascal, Fermat, Huygens Comparative testing of hypotheses Population statistics 1713: Ars Conjectandi by Jacob Bernoulli: Weak law of large numbers Principle of indifference De Moivre (1718): The Doctrine of Chances 6 Bayes and Laplace Paper by Thomas Bayes (1763): inversion
    [Show full text]
  • Arxiv:2002.00269V2 [Cs.LG] 8 Mar 2021
    A Tutorial on Learning With Bayesian Networks David Heckerman [email protected] November 1996 (Revised January 2020) Abstract A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study. 1 Introduction A Bayesian network is a graphical model for probabilistic relationships among a set of variables. arXiv:2002.00269v2 [cs.LG] 8 Mar 2021 Over the last decade, the Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems (Heckerman et al., 1995a).
    [Show full text]
  • Naive Bayes Classifier Assumes That the Presence (Or Absence) of a Particular Feature of a Class Is Unrelated to the Presence (Or Absence) of Any Other Feature
    A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model". In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter. Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple. Depending on the precise nature of the probability model, naive Bayes classifiers can be trained very efficiently in a supervised learning setting. In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without believing in Bayesian probability or using any Bayesian methods. In spite of their naive design and apparently over-simplified assumptions, naive Bayes classifiers have worked quite well in many complex real-world situations. In 2004, analysis of the Bayesian classification problem has shown that there are some theoretical reasons for the apparently unreasonable efficacy of naive Bayes classifiers.[1] Still, a comprehensive comparison with other classification methods in 2006 showed that Bayes classification is outperformed by more current approaches, such as boosted trees or random forests.[2] An advantage of the naive Bayes classifier is that it requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification.
    [Show full text]
  • Bayesian Statistics
    Technical Report No. 2 May 6, 2013 Bayesian Statistics Meng-Yun Lin [email protected] This paper was published in fulfillment of the requirements for PM931 Directed Study in Health Policy and Management under Professor Cindy Christiansen's ([email protected]) direction. Michal Horny, Jake Morgan, Marina Soley Bori, and Kyung Min Lee provided helpful reviews and comments. Table of Contents Executive Summary ...................................................................................................................................... 1 1. Difference between Frequentist and Bayesian .................................................................................... 2 2. Basic Concepts ..................................................................................................................................... 3 3. Bayesian Approach of Estimation and Hypothesis Testing.................................................................. 4 3.1. Bayesian Inference ........................................................................................................................... 4 3.2. Bayesian Prediction .......................................................................................................................... 5 3.3. Bayesian Network ............................................................................................................................. 6 3.4. Software ............................................................................................................................................ 6 4.
    [Show full text]