Estimating a State-Space Model from Point Process Observations

@article{Smith2003EstimatingAS,
  title={Estimating a State-Space Model from Point Process Observations},
  author={Anne C. Smith and Emery N. Brown},
  journal={Neural Computation},
  year={2003},
  volume={15},
  pages={965-991}
}
A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process… CONTINUE READING
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Showing 1-10 of 43 references

Time series analysis of non-gaussian observations based on state space models from both classical and Bayesian perspectives

  • J. Durbin, S. J. Koopman
  • J. Roy. Statist. Soc. B,
  • 2000
Highly Influential
2 Excerpts

Unobserved Monte-Carlo method for identification of partiallyobserved nonlinear state-space systems, part II: counting process observations

  • V. Solo
  • In Proc. IEEE Conference on Decision and Control
  • 2000
Highly Influential
6 Excerpts

Monte Carlo estimation for time series models involving counts

  • K. S. Chan, J. Ledolter
  • J. Am. Stat. Assoc.,
  • 1995
Highly Influential
1 Excerpt

Random point processes in time and space

  • D. L. Snyder, M. I. Miller
  • 1991
Highly Influential
5 Excerpts

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