publication . Article . Preprint . 2011

Rate estimation in partially observed Markov jump processes with measurement errors

Amrein, Michael; Künsch, Hans;
Open Access
  • Published: 26 Mar 2011 Journal: Statistics and Computing, volume 22, pages 513-526 (issn: 0960-3174, eissn: 1573-1375, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
  • Country: Switzerland
We present a simulation methodology for Bayesian estimation of rate parameters in Markov jump processes arising for example in stochastic kinetic models. To handle the problem of missing components and measurement errors in observed data, we embed the Markov jump process into the framework of a general state space model. We do not use diffusion approximations. Markov chain Monte Carlo and particle filter type algorithms are introduced, which allow sampling from the posterior distribution of the rate parameters and the Markov jump process also in data-poor scenarios. The algorithms are illustrated by applying them to rate estimation in a model for prokaryotic aut...
free text keywords: Theoretical Computer Science, Statistics, Probability and Uncertainty, Statistics and Probability, Computational Theory and Mathematics, Variable-order Markov model, Markov process, symbols.namesake, symbols, Markov renewal process, Mathematics, Markov chain mixing time, Markov chain Monte Carlo, Markov model, Statistics, Mathematical optimization, Markov chain, Markov property, Statistics - Computation
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publication . Article . Preprint . 2011

Rate estimation in partially observed Markov jump processes with measurement errors

Amrein, Michael; Künsch, Hans;