Marginal MAP estimation using Markov chain Monte Carlo

@inproceedings{Robert1999MarginalME,
  title={Marginal MAP estimation using Markov chain Monte Carlo},
  author={Christian P. Robert and Arnaud Doucet and Simon J. Godsill},
  booktitle={ICASSP},
  year={1999}
}
Markov chain Monte Carlo (MCMC) methods are powerful simulation-based techniques for sampling from high-dimen sio al and/or non-standard probability distributions. These met hods have recently become very popular in the statistical and signal p rocessing communities as they allow highly complex inference prob lems in detection and estimation to be addressed. However, MCMC is not currently well adapted to the problem of marginal maximum a posteriori (MMAP) estimation. In this paper, we present a… CONTINUE READING
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