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Neural Comput. 2012 Jun;24(6):1462-86. doi: 10.1162/NECO_a_00281. Epub 2012 Feb 24.

Markov chain Monte Carlo methods for state-space models with point process observations.

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  • 1School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.


This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.

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