Despite increasing interest in Bayesian approaches, especially across the social sciences, it has been virtually impossible to find a text that introduces Bayesian data analysis in a manner accessible to social science students. The Bayesian paradigm is ideally suited to the type of data analysis they will have to perform, but the associated mathematics can be daunting. Bayesian Methods: A Social and Behavioral Sciences Approach presents the basic principles of Bayesian statistics in a treatment designed specifically for students in the social sciences and related fields. Requiring few prerequisites, it first introduces Bayesian statistics and inference with detailed descriptions of setting up a probability model, specifying prior distributions, calculating a posterior distribution, and describing the results. This is followed by explicit guidance on assessing model quality and model fit using various diagnostic techniques and empirical summaries. Finally, it introduces hierarchical models within the Bayesian context, which leads naturally to Markov Chain Monte Carlo computing techniques and other numerical methods. The author emphasizes practical computing issues, includes specific details for Bayesian model building and testing, and uses the freely available R and BUGS software for examples and exercise problems. The result is an eminently practical text that is comprehensive, rigorous, and ideally suited to teaching future empirical social scientists.
... 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 ...
These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text.
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 ...
Analyze Repeated Measures Studies Using Bayesian TechniquesGoing beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian ...
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.
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 ...
This book walks you through learning probability and statistics from a Bayesian point of view.
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.
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 .
Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.