Modeling with Normalized Random Measure Mixture Models

MCMC for Normalized Random Measure Mixture Models

This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture models with normalized random measure priors. Making use of some recent posterior characterizations for the class of normalized random measures, we propose novel Markov chain Monte Carlo methods of both marginal type and conditional type. The proposed marginal samplers are ge...

Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models

Normalized random measures (NRMs) provide a broad class of discrete random measures that are often used as priors for Bayesian nonparametric models. Dirichlet process is a well-known example of NRMs. Most of posterior inference methods for NRM mixture models rely on MCMC methods since they are easy to implement and their convergence is well studied. However, MCMC often suffers from slow converg...

Slice sampling normalized kernel-weighted completely random measure mixture models

A number of dependent nonparametric processes have been proposed to model non-stationary data with unknown latent dimensionality. However, the inference algorithms are often slow and unwieldy, and are in general highly specific to a given model formulation. In this paper, we describe a large class of dependent nonparametric processes, including several existing models, and present a slice sampl...

Growth mixture modeling: Analysis with non-Gaussian random effects

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Multivariate latent Gaussian random field mixture models