Lomeli, M;
Favaro, S;
Teh, YW;
(2015)
A hybrid sampler for Poisson-Kingman mixture models.
In: Cortes, C and Lawrence, ND and Lee, DD and Sugiyama, M and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 28 (NIPS 2015).
NIPS Proceedings: Montréal, Canada.
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Abstract
This paper concerns the introduction of a new Markov Chain Monte Carlo scheme for posterior sampling in Bayesian nonparametric mixture models with priors that belong to the general Poisson-Kingman class. We present a novel compact way of representing the infinite dimensional component of the model such that while explicitly representing this infinite component it has less memory and storage requirements than previous MCMC schemes. We describe comparative simulation results demonstrating the efficacy of the proposed MCMC algorithm against existing marginal and conditional MCMC samplers.
| Type: | Proceedings paper |
|---|---|
| Title: | A hybrid sampler for Poisson-Kingman mixture models |
| Event: | Neural Information Processing Systems 2015 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://papers.nips.cc/paper/5799-a-hybrid-sampler... |
| Language: | English |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1543091 |
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