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.
The Particle Learning approach REF uses a fully-adapted filter to learn the parameters of a general class of state space models by defining a particle approximation to the joint posterior distribution of states and conditional sufficient statistics for the fixed parameters.
Particle Learning and Smoothing
Statistical Science 2010, Vol. 25, No. 1, 88-106
Mathematics