Article

Statistics and Computing

, Volume 12, Issue 1, pp 77-84

First online:

Marginal maximum a posteriori estimation using Markov chain Monte Carlo

  • Arnaud Doucet
  • , Simon J. Godsill
  • , Christian P. Robert

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Abstract

Markov chain Monte Carlo (MCMC) methods, while facilitating the solution of many complex problems in Bayesian inference, are not currently well adapted to the problem of marginal maximum a posteriori (MMAP) estimation, especially when the number of parameters is large. We present here a simple and novel MCMC strategy, called State-Augmentation for Marginal Estimation (SAME), which leads to MMAP estimates for Bayesian models. We illustrate the simplicity and utility of the approach for missing data interpolation in autoregressive time series and blind deconvolution of impulsive processes.

Bayesian computation data augmentation deconvolution missing data simulated annealing