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Book Synopsis
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 dif

Bayesian Statistics for Experimental Scientists A

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    A Hardback by Richard A Chechile

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      Book details

      Publisher MIT Press Ltd
      Published 8 September 2020
      ISBN-13 9780262044585
      978-0262044585
      ISBN-10 0262044587
      Also in:
      Mathematics Bayesian inference

      Description

      Book Synopsis
      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 dif

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