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Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models

Arbel, Julyan; Salomond, Jean-Bernard (2016), Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models, NIPS - Conference on Neural Information Processing Systems, p. 7

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Type
Communication / Conférence
Date
2016
Conference title
NIPS - Conference on Neural Information Processing Systems
Conference date
2016-12
Conference city
Barcelone
Conference country
Spain
Book title
NIPS - Conference on Neural Information Processing Systems
Pages
7
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Author(s)
Arbel, Julyan cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Inria Grenoble - Rhône-Alpes
Salomond, Jean-Bernard
Laboratoire d'Analyse et de Mathématiques Appliquées [LAMA]
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
In mixture models, latent variables known as allocation variables play an essential role by indicating, at each iteration, to which component of the mixture observations are linked. In sequential algorithms, these latent variables take on the interpretation of particles. We investigate the use of quasi Monte Carlo within sequential Monte Carlo methods (a technique known as sequential quasi Monte Carlo) in nonparametric mixtures for density estimation. We compare them to sequential and non sequential Monte Carlo algorithms. We highlight a critical distinction of the allocation variables exploration of the latent space under each of the three sampling approaches.
Subjects / Keywords
Density estimation; Bayesian Nonparametrics; Quasi random variables; Monte Carlo methods; Sequential sampling