Marginal maximum a posteriori estimation using Markov chain Monte Carlo
Marginal maximum a posteriori estimation using Markov chain Monte Carlo
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DOI：
10.1023/a:1013172322619
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发表时间：
2002-01-01
影响因子：
2.2
通讯作者：
Robert, CP
中科院分区：
文献类型：
--
作者：
Doucet, A;Godsill, SJ;Robert, CP
马尔可夫链蒙特卡罗（MCMC）方法，虽然促进了贝叶斯推理中的许多复杂问题的解决方案，目前并不很好地适应边际最大后验概率（MMAP）估计的问题，特别是当参数的数量是大的。我们在这里提出了一个简单而新颖的MCMC策略，称为边际估计的状态增强（State-Augmentation for Marginal Estimation，简称STAR），它导致贝叶斯模型的MMAP估计。我们说明了简单和实用的方法缺失的数据插值自回归时间序列和脉冲过程的盲反卷积。
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.