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Inference for dynamic and latent variable models via iterated, perturbed Bayes maps.

ABSTRACT: Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process.

SUBMITTER: Ionides EL

PROVIDER: S-EPMC4311819 | biostudies-other | 2015 Jan

REPOSITORIES: biostudies-other

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Inference for dynamic and latent variable models via iterated, perturbed Bayes maps.

Ionides Edward L EL Nguyen Dao D Atchadé Yves Y Stoev Stilian S King Aaron A AA

Proceedings of the National Academy of Sciences of the United States of America 20150107 3

Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory d ...[more]

PMID: 25568084

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