The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorpora
... 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.
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 ...
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.
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 .
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 ...
Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
Descriptions of most methods can be found in the book by Mann, Schafer and Singpurwalla (1974). In general the method of maximum likelihood is the most useful of the classical approaches.
"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels.