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Practical Bayesian Inference - A Primer for Physical Scientists (Paperback)
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Practical Bayesian Inference - A Primer for Physical Scientists (Paperback)
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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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