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Toward practical N2 Monte Carlo: The Marginal Particle Filter

Abstract:

Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dynamic models. These methods allow us to approximate the joint posterior distribution using sequential importance sampling. In this framework, the dimension of the target distribution grows with each time step, thus it is necessary to introduce some resampling steps to ensure that the estimates provided by the algorithm have a reasonable variance. In many applications, we are only interested in the ...

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Authors


De Freitas, N More by this author
Journal:
Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005
Pages:
308-315
Publication date:
2005
URN:
uuid:3b66de32-b18b-4522-8c4e-3479915697a0
Source identifiers:
186428
Local pid:
pubs:186428
Language:
English

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