Efficient estimation of likelihood ratios in state-space models–application to scalable exact approximations of MCMCs
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Yıldırım, Sinan and Andrieu, Christophe and Doucet, Arnaud (2015) Efficient estimation of likelihood ratios in state-space models–application to scalable exact approximations of MCMCs. [Working Paper / Technical Report] Sabanci University ID:UNSPECIFIED
We present a new class of exact-approximate Markov chain Monte Carlo methods for static parameter estimation in hidden Markov models. Like the particle marginal Metropolis-Hastings (PMMH) algorithm, the proposed algorithms target the joint posterior distribution of the latent states while they ‘mimic’ the marginal Metropolis-Hastings algorithm for the static parameter. In contrast to PMMH, these algorithms estimate the acceptance ratio directly without having to use of the same estimate in its denominator, which is a well-known cause for stickiness in pseudo-marginal type algorithms.
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