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Unbiased Smoothing using Particle Independent Metropolis-Hastings
TLDR
In this paper, a particle independent metropolis-hastings (PIMH) method is proposed to produce unbiased smoothing estimators for the distribution of a latent Markov process given noisy measurements.Abstract:
We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements. This is known as the smoothing problem and is often approached with particle and Markov chain Monte Carlo (MCMC) methods. These methods provide consistent but biased estimators when run for a finite time. We propose a simple way of coupling two MCMC chains built using Particle Independent Metropolis-Hastings (PIMH) to produce unbiased smoothing estimators. Unbiased estimators are appealing in the context of parallel computing, and facilitate the construction of confidence intervals. The proposed scheme only requires access to off-the-shelf Particle Filters (PF) and is thus easier to implement than recently proposed unbiased smoothers. The approach is demonstrated on a Levy-driven stochastic volatility model and a stochastic kinetic model.read more
References
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Journal ArticleDOI
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
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On adaptive resampling strategies for sequential Monte Carlo methods
TL;DR: In this paper, the convergence analysis of a class of sequential Monte Carlo (SMC) methods where the times at which resampling occurs are computed online using criteria such as the effective sample size is studied.
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