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This accessible book teaches readers how to perform data science through examples and figures. The inner workings of linear regression, non-linear models and Monte Carlo methods are illustrated. The art and craft of data science is demonstrated.
The approach is a Bayesian one. So, while every scientist agrees on the value of good data, most shy away when discussing the motivation of their priors. But suitable priors are equally important as good data. This tutorial guides the reader in constructing useful priors.
The main objective of this tutorial is learning to choose between alternative, credible-looking models. Along the route, many of the author's pitfalls are pointed out.
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