Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.

@article{Stathopoulos2013MarkovCM,
  title={Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.},
  author={Vassilios Stathopoulos and Mark A. Girolami},
  journal={Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
  year={2013},
  volume={371 1984},
  pages={20110541}
}
Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both… CONTINUE READING
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