Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models

Abstract : 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.
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Communication dans un congrès
NIPS - Conference on Neural Information Processing Systems, Dec 2016, Barcelone, Spain
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Julyan Arbel, Jean-Bernard Salomond. Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models. NIPS - Conference on Neural Information Processing Systems, Dec 2016, Barcelone, Spain. 〈hal-01405568〉

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