Marginal maximum a posteriori estimation using Markov chain Monte Carlo

  title={Marginal maximum a posteriori estimation using Markov chain Monte Carlo},
  author={Arnaud Doucet and Simon J. Godsill and Christian P. Robert},
  journal={Statistics and Computing},
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… CONTINUE READING
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