Estimating a state-space model from point process observations
Neural Computation
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Efficient markov chain monte carlo methods for decoding neural spike trains
Neural Computation
Computational Statistics & Data Analysis
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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.