Recursive Monte Carlo algorithms for parameter estimation in general state space models

C Andrieu,A Doucet

doi:10.1109/ssp.2001.955210

Recursive Monte Carlo algorithms for parameter estimation in general state space models

C Andrieu, A Doucet

https://doi.org/10.1109/ssp.2001.955210

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Publication Date: Aug 6, 2001

Citations: 5

Affiliation: University of Bristol, University of Melbourne

#New Class Of Algorithms #Monte Carlo Markov Chain + Show 8 more

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Abstract

We present new algorithms that aim at estimating the parameters of a latent variable process in an on-line manner. This new class of on-line algorithms is inspired by Monte Carlo Markov chain (MCMC) methods whose use has been mainly restricted to static problems, i.e., for which the set of observations is fixed. The main interest of this new class of algorithms is that it combines MCMC and particle filtering techniques, for which extensive know-how and literature are now available.

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