Description
An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis.
This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics.
Table of Contents
I Introduction to Bayesian Analysis for Categorical Data
1 Probability and Inference
2 Binomial Model
3 Multinomial Data
4 Condition Effects: Categorical Data
II Bayesian Analysis of Ordinal Information
5 Median- and Sign-Based Methods
6 Wilcoxon Signed-Rank Procedure
7 Mann-Whitney Procedure
8 Distribution-Free Correlation
References
Index
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- REV SPEED 2017年2月号



