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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

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Volume 14, Issue 2


Volume 10 (2011)

Volume 9 (2010)

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Volume 1 (2002)

Likelihood free inference for Markov processes: a comparison

Jamie Owen
  • Corresponding author
  • School of Mathematics and Statistics, Newcastle University, Herschel Building, Newcastle upon Tyne, NE1 7RU, UK
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Darren J. Wilkinson
  • School of Mathematics and Statistics, Newcastle University, Herschel Building, Newcastle upon Tyne, NE1 7RU, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Colin S. Gillespie
  • School of Mathematics and Statistics, Newcastle University, Herschel Building, Newcastle upon Tyne, NE1 7RU, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2015-01-24 | DOI: https://doi.org/10.1515/sagmb-2014-0072


Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and “likelihood free” Markov chain Monte Carlo techniques are popular methods for tackling inference in these scenarios but such techniques are computationally expensive. In this paper we compare the two approaches to inference, with a particular focus on parameter inference for stochastic kinetic models, widely used in systems biology. Discrete time transition kernels for models of this type are intractable for all but the most trivial systems yet forward simulation is usually straightforward. We discuss the relative merits and drawbacks of each approach whilst considering the computational cost implications and efficiency of these techniques. In order to explore the properties of each approach we examine a range of observation regimes using two example models. We use a Lotka–Volterra predator–prey model to explore the impact of full or partial species observations using various time course observations under the assumption of known and unknown measurement error. Further investigation into the impact of observation error is then made using a Schlögl system, a test case which exhibits bi-modal state stability in some regions of parameter space.

Keywords: ABC; likelihood free; particle MCMC; stochastic kinetic model; systems biology


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About the article

Corresponding author: Jamie Owen, School of Mathematics and Statistics, Newcastle University, Herschel Building, Newcastle upon Tyne, NE1 7RU, UK, e-mail:

Published Online: 2015-01-24

Published in Print: 2015-04-01

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 14, Issue 2, Pages 189–209, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2014-0072.

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