\dm_csml_event_details UCL ELLIS

Bayesian inference for nonparametric mixture models with intractable normalizing constants


Isadora Antoniano-Villalobos


Department of Decision Sciences, Bocconi University, Italy


Friday, 10 May 2013






Malet Place Eng 1.03

Event series

DeepMind/ELLIS CSML Seminar Series


Since the advent of Bayesian posterior inference via simulation techniques, it has been possible to estimate Bayesian nonparametric models. While the mixture of Dirichlet process (MDP) model remains one of the most popular, the advances in MCMC methods have now allowed models to move away from standard setups involving independent and identically distributed observations, to cover more complex data structures, such as regression models and time series models.

In this talk, we discuss some models for which the normalizing constant for the likelihood function involves an infinite sum, making it intractable. In such cases, it is not possible to apply directly the variety of MCMC schemes currently available for simulation from the posterior distributions of infinite mixture models. We propose a latent variable extension for the intractable models, involving auxiliary variables which are themselves infinite-dimensional. We then discuss inference for such extended models, via simulation techniques which combine the now popular slice sampling method for infinite mixture models, with trans-dimensional MCMC ideas.

Slides for the talk: PDF