Efficient Bayesian inference for dynamic mixture models

A Bayesian approach is presented for estimating a mixture of linear Gaussia n stale space models. Such models are used to model interventions in time s eries and nonparametric regression. Markov chain Monte Carlo sampling is us ually necessary to obtain the posterior distributions of such mixture model s, because it is difficult to obtain them analytically. The methodological contribution of the article is to derive a set of recursions for dynamic mi xture models that efficiently implement a Markov chain Monte Carlo sampling scheme that converges rapidly to the posterior distribution. The methodolo gy is illustrated by fitting an autoregressive model subject to interventio ns to zinc concentration in sludge.