Imperial College London
Alternate

Dr James S. Martin

Faculty of Natural Sciences, Department of Mathematics

Senior Strategic Teaching Fellow in Data Science
 
 
 
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Contact

 

james.martin

 
 
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Location

 

Roderic Hill BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Martin:2014:10.1080/07362994.2013.879262,
author = {Martin, JS and Jasra, A and Singh, SS and Whiteley, N and Del, Moral P and McCoy, E},
doi = {10.1080/07362994.2013.879262},
journal = {Stochastic Analysis and Applications},
pages = {397--420},
title = {Approximate Bayesian computation for smoothing},
url = {http://dx.doi.org/10.1080/07362994.2013.879262},
volume = {32},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Martin,JS
AU  - Jasra,A
AU  - Singh,SS
AU  - Whiteley,N
AU  - Del,Moral P
AU  - McCoy,E
DO  - 10.1080/07362994.2013.879262
EP  - 420
PY  - 2014///
SN  - 0736-2994
SP  - 397
TI  - Approximate Bayesian computation for smoothing
T2  - Stochastic Analysis and Applications
UR  - http://dx.doi.org/10.1080/07362994.2013.879262
UR  - http://hdl.handle.net/10044/1/64619
VL  - 32
ER  -
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