Bayesian Statistics for Experimental Scientists
A General Introduction Using Distribution-Free Methods
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.
The book first covers elementary probability theory, the binomial model, the multinomial model, and methods for comparing different experimental conditions or groups. It then turns its focus to distribution-free statistics that are based on having ranked data, examining data from experimental studies and rank-based correlative methods. Each chapter includes exercises that help readers achieve a more complete understanding of the material.
The book devotes considerable attention not only to the linkage of statistics to practices in experimental science but also to the theoretical foundations of statistics. Frequentist statistical practices often violate their own theoretical premises. The beauty of Bayesian statistics, readers will learn, is that it is an internally coherent system of scientific inference that can be proved from probability theory.
Pre-Order Hardcover$65.00 X ISBN: 9780262044585 472 pp. | 7 in x 9 in 50 figures
“A welcome effort to include Bayesian foundations in the early training of scientists and statisticians…. [Chechile] discusses important foundational topics, without restoring to mystifying mathematical arguments.”
“Through the exemplification of the inferential aspect of the binomial mode, the book introduces a few important concepts in Bayesian statistics, including the Fisher Invariance principle, the Jeffreys prior, the concept of noninformative versus informative priors, Bayes factors and statistical decision making. Chapters are extremely well written…and the discussion of the likelihood principle and the meta-analysis are also non-standard and outstanding.”