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 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.
Johannes, Michael, Carlos Carvalho, Hedibert Lopes, and Nicholas Polson. "Particle Learning for Sequential Bayesian Computation." In Bayesian Statistics 9. Ed. José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West. Oxford: Oxford University Press, October 2011.
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