"Particle Learning and Smoothing"
©
Statistical Science,
2010
Volume: 25
|
Issue: 1
|
Pages: 88-106
Publication type: Journal article
Research Archive Topic: Business Economics and Public Policy, Corporate Finance
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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