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Article overview
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Pseudo-Marginal Hamiltonian Monte Carlo with Efficient Importance Sampling | Kjartan Kloster Osmundsen
; Tore Selland Kleppe
; Roman Liesenfeld
; | Date: |
19 Dec 2018 | Abstract: | The joint posterior of latent variables and parameters in Bayesian
hierarchical models often has a strong nonlinear dependence structure, thus
making it a challenging target for standard Markov-chain Monte-Carlo methods.
Pseudo-marginal methods aim at effectively exploring such target distributions,
by marginalizing the latent variables using Monte-Carlo integration and
directly targeting the marginal posterior of the parameters. We follow this
approach and propose a generic pseudo-marginal algorithm for efficiently
simulating from the posterior of the parameters. It combines efficient
importance sampling, for accurately marginalizing the latent variables, with
the recently developed pseudo-marginal Hamiltonian Monte Carlo approach. We
illustrate our algorithm in applications to dynamic state space models, where
it shows a very high simulation efficiency even in challenging scenarios with
complex dependence structures. | Source: | arXiv, 1812.7929 | Services: | Forum | Review | PDF | Favorites |
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