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Scalable inference for Markov processes with intractable likelihoods

Lookup NU author(s): Jamie Owen, Professor Darren Wilkinson, Dr Colin Gillespie

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo (MCMC) techniques can lead to exact inference in such models but in practice can suffer performance issues including long burn-in periods and poor mixing. On the other hand approximate Bayesian computation techniques can allow rapid exploration of a large parameter space but yield only approximate posterior distributions. Here we consider the combined use of approximate Bayesian computation and MCMC techniques for improved computational efficiency while retaining exact inference on parallel hardware.


Publication metadata

Author(s): Owen J, Wilkinson DJ, Gillespie CS

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2015

Volume: 25

Issue: 1

Pages: 145-156

Print publication date: 01/01/2015

Online publication date: 01/11/2014

Acceptance date: 16/10/2014

Date deposited: 02/12/2014

ISSN (print): 0960-3174

ISSN (electronic): 1573-1375

Publisher: Springer US

URL: http://dx.doi.org/10.1007/s11222-014-9524-7

DOI: 10.1007/s11222-014-9524-7


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