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Bayesian Data Analysis, Third Edition, 3rd Edition by Donald B. Rubin, Aki Vehtari, David B. Dunson, Hal S. Stern, John B. Carlin, Andrew Gelman

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Chapter 16

Generalized linear models

This chapter reviews generalized linear models from a Bayesian perspective. We discuss prior distributions and hierarchical models. We demonstrate how to approximate the likelihood by a normal distribution, as an approximation and as a step in more general computations. Finally, we discuss the class of loglinear models, a subclass of Poisson generalized linear models that is commonly used for missing data imputation and discrete multivariate outcomes. This chapter is not intended to be exhaustive, but rather ...

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