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Particle Learning and Smoothing
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DOI：10.1214/10-STS325.png)
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Abstract
En 中文
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.
Keywords:
Mixture Kalman filter
parameter learning
particle learning
sequential inference
smoothing
state filtering
state space models
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