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      Particle Learning and Smoothing

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          Abstract

          Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.

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          Sequential Monte Carlo Methods for Dynamic Systems

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            An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

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              Combined Parameter and State Estimation in Simulation-Based Filtering

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                Author and article information

                Journal
                2010-11-04
                Article
                10.1214/10-STS325
                1011.1098
                31f1c1d1-2726-4ca0-99a9-e42b7c588db0

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                IMS-STS-STS325
                Statistical Science 2010, Vol. 25, No. 1, 88-106
                Published in at http://dx.doi.org/10.1214/10-STS325 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
                stat.ME
                vtex

                Methodology
                Methodology

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