Particle Learning for Sequential Bayesian Computation*
Hedibert F. Lopes, Michael Slater Johannes, Carlos M. Carvalho, Nicholas G. Polson · Oxford University Press eBooks · 2011
Particle learning provides a simulation‐based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive distribution and propagation rule to build a resampling‐sampling framework. Predictive inference and sequential Bayes factors are a direct by‐product. Our approach provides a simple yet powerful framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.