Bayesian statistics directed towards mainstream statistics. How to infer scientific, medical, and social conclusions from numerical data.
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 ...
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 ...
This book walks you through learning probability and statistics from a Bayesian point of view.
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 ...
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.
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.