Description: 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.
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EAN: 9781107192119
UPC: 9781107192119
ISBN: 9781107192119
MPN: N/A
Item Length: 25.8 cm
Number of Pages: 304 Pages
Publication Name: Practical Bayesian Inference: a Primer for Physical Scientists
Language: English
Publisher: Cambridge University Press
Item Height: 253 mm
Subject: Mathematics, Physics
Publication Year: 2017
Type: Textbook
Item Weight: 780 g
Author: Coryn A. L. Bailer-Jones
Item Width: 178 mm
Format: Hardcover
