A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

A Bayesian nonparametric model is a Bayesian model on an infinite-dimensional parameter
space. The parameter space is typically chosen as the set of all possible solutions for a …

Bayesian Model Selection and Model Averaging Page 1 Journal of Mathematical
Psychology 44, 92 107 (2000) Bayesian Model Selection and Model Averaging Larry …

We review inference under models with nonparametric Bayesian (BNP) priors. The
discussion follows a set of examples for some common inference problems. The examples …

Bayesian nonparametric (BNP) models are becoming increasingly important in psychology,
both as theoretical models of cognition and as analytic tools. However, existing tutorials tend …

Many statistical scenarios initially involve several candidate models that describe the data-
generating process. Analysis often proceeds by first selecting the best model according to …

In this book, we review nonparametric Bayesian methods and models. The organization of
the book follows a data analysis perspective. Rather than focusing on specific models …

A compilation of original articles by Bayesian experts, this volume presents perspectives on
recent developments on nonparametric and semiparametric methods in Bayesian statistics …

There has been increasing interest in applying Bayesian nonparametric methods in large
samples and high dimensions. As Markov chain Monte Carlo (MCMC) algorithms are often …

An important aspect of mixture modeling is the selection of the number of mixture
components. In this paper, we discuss the Bayes factor as a selection tool. The discussion …