Particle Methods as Message Passing

Justin Dauwels, Sascha Korl, Hans-Andrea Loeliger

Abstract: It is shown how particle methods can be viewed as message passing on factor graphs. In this setting, particle methods can readily be combined with other message-passing techniques such as the sum-product and max-product algorithm, expectation maximization, iterative conditional modes, steepest descent, Kaiman filters, etc. Generic message computation rules for particle-based representations of sum-product messages are formulated. Various existing particle methods are described as instances of those generic rules, i.e., Gibbs sampling, importance sampling, Markov-chain Monte Carlo methods (MCMC), particle filtering, and simulated annealing