马尔可夫链蒙特卡罗算法

Proceedings of 1994 Workshop on Information Theory and Statistics Pub Date : 1994-10-27 DOI:10.1109/WITS.1994.513879

J. Rosenthal

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引用次数: 0

摘要

我们简要介绍了马尔可夫链蒙特卡罗算法，如Gibbs采样器和Metropolis-Hastings(1953,1970)算法，这些算法在统计文献中经常用于探索复杂的概率分布。我们提出了一种证明严格的、先验的边界和实现算法收敛所需的迭代次数的一般方法。

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Markov chain Monte Carlo algorithms

We briefly describe Markov chain Monte Carlo algorithms, such as the Gibbs sampler and the Metropolis-Hastings (1953, 1970) algorithm, which are frequently used in the statistics literature to explore complicated probability distributions. We present a general method for proving rigorous, a priori bounds an the number of iterations required to achieve convergence of the algorithms.

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Proceedings of 1994 Workshop on Information Theory and Statistics

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