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Abstract: We discuss simulation-based sequential analysis - or particle filtering - in dynamic models, with a focus on sequential Bayesian learning about time-varying state vectors and fixed model parameters simultaneously. This includes a general approach that combines old ideas of smoothing using kernel methods with newer ideas of auxiliary particle filtering. We highlight specific smoothing approaches that have interpretation as adding "artificial evolution noise" to fixed model parameters at each... (Update)
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BibTeX entry: (Update)
Liu, J. and West, M. (2000). Combined parameter and state estimation in simulation-based filtering. In A. Doucet, J. F. G. De Freitas and N. J. Gordon (eds.), Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag New York. http://citeseer.ist.psu.edu/liu00combined.html More
@inproceedings{ liu00combined,
author = "J. Liu and M. West",
title = "Combined parameter and state estimation in simulation-based filtering",
editor = "A. Doucet, J. F. G. De Freitas and N. J. Gordon",
booktitle = "Sequential Monte Carlo Methods in Practice. New York",
publisher = "Springer-Verlag, New York",
year = "2000",
url = "citeseer.ist.psu.edu/liu00combined.html" }
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