An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on justifying procedures. The expanded examples reflect this updated approach. New to the Third Edition A chapter on Bayesian decision theory, covering Bayesian and frequentist decision theory as well as the connection of empirical Bayes with James–Stein estimation A chapter on the practical implementation of MCMC methods using the BUGS software Greatly expanded chapter on hierarchical models that shows how this area is well suited to the Bayesian paradigm Many new applications from a variety of social science disciplines Double the number of exercises, with 20 now in each chapter Updated BaM package in R, including new datasets, code, and procedures for calling BUGS packages from R This bestselling, highly praised text continues to be suitable for a range of courses, including an introductory course or a computing-centered course. It shows students in the social and behavioral sciences how to use Bayesian methods in practice, preparing them for sophisticated, real-world work in the field.
The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite ...
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. Murray, J. S., Dunson, D. B., Carin, L., and Lucas, J. E. (2013). ... Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo ...
This book walks you through learning probability and statistics from a Bayesian point of view.
The book also discusses the theory and practical use of MCMC methods.
These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text.
... Bayesian Methods for Nonlinear Classification and Regression, Iohn Wiley 8: Sons. Inc., 2002, New York. Hand, DI and Taylor, C.C. Multivariate Analysis of Variance and Repeated Measures, Chapman 8: Hall, 1987, London, UK. Thall, PF. and ...
The first part of this book presents the foundations of Bayesian inference, via simple inferential problems in the social sciences: proportions, cross-tabulations, counts, means and regression analysis.
Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
One can then apply Bayesian model selection to determine the favoured number of sources by finding the model that maximizes ENcvr(NC). References Barreiro, R. B., Hobson, ... Numerical Bayesian Methods Applied to Signal Processing.
Here Halpern discussed a general approach to fitting a piecewise linear function ( which is equivalent to a linear spline ) to a scatterplot using a conjugate prior specification which is very similar to that described in this chapter .