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
Read more at http://www.cambridge.org/gb/academic/subjects/physics/mathematical-methods/practical-bayesian-inference-primer-physical-scientists#wqbyKeKHR9uVtZHz.99
Reviews & endorsements
'Coryn Bailer-Jones provides a coherent introduction to the most important modern statistical methods and computational tools for analysing data. His writing style is easy to follow, without the burden of formal proofs and complex derivations, but with sufficient mathematical rigour. This book could be used as an excellent textbook for a semester-long course aimed at undergraduate and graduate students of physical sciences and engineering (knowledge of basic calculus is assumed, but no specific experience with probability or statistics is required). Theoretical concepts and examples of applications are extensively illustrated and supported by author's code in the R language.' Željko Ivezić, University of Washington
Read more at http://www.cambridge.org/gb/academic/subjects/physics/mathematical-methods/practical-bayesian-inference-primer-physical-scientists#wqbyKeKHR9uVtZHz.99
R Code available at:
http://www.mpia.de/~calj/pbi/PBI_scripts.zip
Reference:
http://www.cambridge.org/de/academic/subjects/physics/mathematical-methods/practical-bayesian-inference-primer-physical-scientists?format=PB
Errata:
http://www.mpia.de/~calj/pbi/errata.pdf