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Introduction and examples.- Belief, probability and exchangeability.- One parameter models.- Monte Carlo approximation.- The normal model.- Posterior approximation with the Gibbs sampler.- The multivariate normal model.- Group comparisons and hierarchical modeling.- Linear regression.- Nonconjugate priors and the Metropolis-Hastings algorithm.- Linear and generalized linear mixed effects models.- Latent variable methods for ordinal data.
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Add A First Course in Bayesian Statistical Methods, A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run as-is allow the reader to perform the data analyses themselves. , A First Course in Bayesian Statistical Methods to the inventory that you are selling on WonderClubX
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Add A First Course in Bayesian Statistical Methods, A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run as-is allow the reader to perform the data analyses themselves. , A First Course in Bayesian Statistical Methods to your collection on WonderClub |