CUED Publications database

Approximate Bayesian Computation for Smoothing

Martin, JS and Jasra, A and Singh, SS and Whiteley, N and Del Moral, P and McCoy, E (2014) Approximate Bayesian Computation for Smoothing. Stochastic Analysis and Applications, 32. pp. 397-420. ISSN 0736-2994

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Abstract

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter ε > 0. We provide theoretical results which quantify, in terms of ε, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is, where n is the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. © Taylor & Francis Group, LLC.

Item Type: Article
Uncontrolled Keywords: Approximate Bayesian Computation Hidden Markov models Sequential Monte Carlo Smoothing
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 29 May 2014 17:45
Last Modified: 08 Dec 2014 02:20
DOI: 10.1080/07362994.2013.879262