Bayesian nonparametric and semiparametric mixture models have become extremely popular in the last 10 years because they provide flexibility and interpretability while preserving computational simplicity. This book is a contribution to this growing literature, discussing the design of models for collections of distributions and their application to density estimation and nonparametric regression. All methods introduced in this book are discussed in the context of complex scientific applications in public health, epidemiology and finance.
Some Advances in Bayesian Nonparametric Modeling: Nested Dirichlet process and nonparametric regression
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