Practical Bayesian Inference: A Primer for Physical Scientists.

Synopsis

Science is fundamentally about learning from data, and doing so in the presence of uncertainty. This volume is an introduction to the major concepts of probability and statistics, and the computational tools for analysing and interpreting data. It describes the Bayesian approach, and explains how this can be used to fit and compare models in a range of problems. Topics covered include regression, parameter estimation, model assessment, and Monte Carlo methods, as well as widely used classical methods such as regularization and hypothesis testing. The emphasis throughout is on the principles, the unifying probabilistic approach, and showing how the methods can be implemented in practice. R code (with explanations) is included and is available online, so readers can reproduce the plots and results for themselves. Aimed primarily at undergraduate and graduate students, these techniques can be applied to a wide range of data analysis problems beyond the scope of this work. Written in an informal yet precise style, suitable for a wide audience from a range of backgrounds in the physical sciences Promotes the Bayesian approach as a general framework for solving problems, but also makes comparison with frequentist methods Describes how methods can be applied in practice to the readers' own problems, so it is not simply a recipe book The R code from the book is available freely online, allowing readers to see how the theory is actually implemented and reproduce the plots and results in the book

Copyright:

2017

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9781108126434

Publisher:

Cambridge University Press

Date of Addition:

08/25/17

Copyrighted By:

Coryn A. L. Bailer-Jones

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Textbooks, Mathematics and Statistics

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.