Computational Bayesian data analysis

Résumé fourni par la source

This chapter introduces the basic usage of the brms library in R, focusing on a model with a normal likelihood. The chapter demonstrates how the posterior distribution of a parameter can be derived using sampling rather than through the analytical approach discussed in chapter 2. The sensitivity of the posterior to the prior specification is discussed, and different categories of prior (ranging from flat, uninformative priors to informative priors) are summarized. Two other important concepts introduced are prior and posterior predictive distributions. The influence of the likelihood function in determining the posterior is also discussed.

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