Bayesian estimation of filtered point processes using Markov chain Monte Carlo methods

@article{Andrieu1997BayesianEO,
  title={Bayesian estimation of filtered point processes using Markov chain Monte Carlo methods},
  author={Christophe Andrieu and Antoine Doucet and Patrick Duvaut},
  journal={Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)},
  year={1997},
  volume={2},
  pages={1097-1100 vol.2}
}
Filtered point processes model a huge amount of physical phenomena. Usually, only noisy observations are in practice available. From these data, one would like to estimate the parameters of the filtered point process. This is a complex problem which in general does not admit any closed-form solution. In this paper, we propose stochastic algorithms to perform statistical estimation for such processes in a Bayesian framework. These algorithms rely on Markov chain Monte Carlo methods which are… CONTINUE READING

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