Probability and Bayesian Modeling: (Chapman & Hall/CRC Texts in Statistical Science)

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. 　

Table of Contents:
Probability: A Measurement of Uncertainty Introduction The Classical View of a Probability The Frequency View of a Probability The Subjective View of a Probability The Sample Space Assigning Probabilities Events and Event Operations The Three Probability Axioms The Complement and Addition Properties Exercises Counting Methods Introduction: Rolling Dice, Yahtzee, and Roulette Equally Likely Outcomes The Multiplication Counting Rule Permutations Combinations Arrangements of Non-Distinct Objects Playing Yahtzee Exercises Conditional Probability Introduction: The Three Card Problem In Everyday Life In a Two-Way Table Definition and the Multiplication Rule The Multiplication Rule Under Independence Learning Using Bayes' Rule R Example: Learning About a Spinner Exercises Discrete Distributions Introduction: The Hat Check Problem Random Variable and Probability Distribution Summarizing a Probability Distribution Standard Deviation of a Probability Distribution Coin-Tossing Distributions Binomial probabilities Binomial computations Mean and standard deviation of a Binomial Negative Binomial Experiments Exercises Continuous Distributions Introduction: A Baseball Spinner Game The Uniform Distribution Probability Density: Waiting for a Bus The Cumulative Distribution Function Summarizing a Continuous Random Variable Normal Distribution Binomial Probabilities and the Normal Curve Sampling Distribution of the Mean Exercises Joint Probability Distributions Introduction Joint Probability Mass Function: Sampling From a Box Multinomial Experiments Joint Density Functions Independence and Measuring Association Flipping a Random Coin: The Beta-Binomial Distribution Bivariate Normal Distribution Exercises Learning About a Binomial Probability Introduction: Thinking About a Proportion Subjectively Bayesian Inference with Discrete Priors Example: students' dining preference Discrete prior distributions for proportion p Likelihood of proportion p Posterior distribution for proportion p Inference: students' dining preference Discussion: using a discrete prior Continuous Priors The Beta distribution and probabilities Choosing a Beta density curve to represent prior opinion Updating the Beta Prior Bayes' rule calculation From Beta prior to Beta posterior: conjugate priors Bayesian Inferences with Continuous Priors Bayesian hypothesis testing Bayesian credible intervals Bayesian prediction Predictive Checking Exercises Modeling Measurement and Count Data Introduction Modeling Measurements Examples The general approach Outline of chapter Bayesian Inference with Discrete Priors Example: Roger Federer's time-to-serve Simplification of the likelihood Inference: Federer's time-to-serve Continuous Priors The Normal prior for mean _ Choosing a Normal prior Updating the Normal Prior Introduction A quick peak at the update procedure Bayes' rule calculation Conjugate Normal prior Bayesian Inferences for Continuous Normal Mean Bayesian hypothesis testing and credible interval Bayesian prediction Posterior Predictive Checking Modeling Count Data Examples The Poisson distribution Bayesian inferences Case study: Learning about website counts Exercises Simulation by Markov Chain Monte Carlo Introduction The Bayesian computation problem Choosing a prior The two-parameter Normal problem Overview of the chapter Markov Chains Definition Some properties Simulating a Markov chain The Metropolis Algorithm Example: Walking on a number line The general algorithm A general function for the Metropolis algorithm Example: Cauchy-Normal problem Choice of starting value and proposal region Collecting the simulated draws Gibbs Sampling Bivariate discrete distribution Beta-Binomial sampling Normal sampling { both parameters unknown MCMC Inputs and Diagnostics Burn-in, starting values, and multiple chains Diagnostics Graphs and summaries Using JAGS Normal sampling model Multiple chains Posterior predictive checking Comparing two proportions Exercises Bayesian Hierarchical Modeling Introduction Observations in groups Example: standardized test scores Separate estimates? Combined estimates? A two-stage prior leading to compromise estimates Hierarchical Normal Modeling Example: ratings of animation movies A hierarchical Normal model with random _ Inference through MCMC Hierarchical Beta-Binomial Modeling Example: Deaths after heart attack A hierarchical Beta-Binomial model Inference through MCMC Exercises Simple Linear Regression Introduction Example: Prices and Areas of House Sales A Simple Linear Regression Model A Weakly Informative Prior Posterior Analysis Inference through MCMC Bayesian Inferences with Simple Linear Regression Simulate fits from the regression model Learning about the expected response Prediction of future response Posterior predictive model checking Informative Prior Standardization Prior distributions Posterior Analysis A Conditional Means Prior Exercises Bayesian Multiple Regression and Logistic Models Introduction Bayesian Multiple Linear Regression Example: expenditures of US households A multiple linear regression model Weakly informative priors and inference through MCMC Prediction Comparing Regression Models Bayesian Logistic Regression Example: US women labor participation A logistic regression model Conditional means priors and inference through MCMC Prediction Exercises Case Studies Introduction Federalist Papers Study Introduction Data on word use Poisson density sampling Negative Binomial sampling Comparison of rates for two authors Which words distinguish the two authors? Career Trajectories Introduction Measuring hitting performance in baseball A hitter's career trajectory Estimating a single trajectory Estimating many trajectories by a hierarchical model Latent Class Modeling Two classes of test takers A latent class model with two classes Disputed authorship of the Federalist Papers Exercises Appendix Appendix A: The constant in the Beta posterior Appendix B: The posterior predictive distribution Appendix C: Comparing Bayesian models

About the Author :
Jim Albert is a Distinguished University Professor of Statistics at Bowling Green State University. His research interests include Bayesian modeling and applications of statistical thinking in sports. He has authored or coauthored several books including Ordinal Data Modeling, Bayesian Computation with R, and Workshop Statistics: Discovery with Data, A Bayesian Approach. Jingchen (Monika) Hu is an Assistant Professor of Mathematics and Statistics at Vassar College. She teaches an undergraduate-level Bayesian Statistics course at Vassar, which is shared online across several liberal arts colleges. Her research focuses on dealing with data privacy issues by releasing synthetic data.