THE PSEUDO-MARGINAL APPROACH FOR EFFICIENT MONTE CARLO COMPUTATIONS
THE PSEUDO-MARGINAL APPROACH FOR EFFICIENT MONTE CARLO COMPUTATIONS
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DOI：
10.1214/07-aos574
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发表时间：
2009-04-01
影响因子：
4.5
通讯作者：
Roberts, Gareth O.
中科院分区：
文献类型：
--
作者：
Andrieu, Christophe;Roberts, Gareth O.
我们介绍了一个强大的和灵活的MCMC算法的随机模拟。该方法建立在最初在[Genetics 164（2003）1139-1160]中引入的伪边缘方法上，示出了作为理想化边缘算法的近似的算法如何能够共享与理想化方法相同的边缘平稳分布。理论结果给出描述所提出的方法的收敛特性，并给出简单的数值例子来说明有前途的经验特性的技术。有趣的比较，一个更明显的，但不精确的，蒙特卡罗近似的边际算法，也给出了。
We introduce a powerful and flexible MCMC algorithm for stochastic simulation. The method builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139-1160], showing how algorithms which are approximations to an idealized marginal algorithm, can share the same marginal stationary distribution as the idealized method. Theoretical results are given describing the convergence properties of the proposed method, and simple numerical examples are given to illustrate the promising empirical characteristics of the technique. Interesting comparisons with a more obvious, but inexact, Monte Carlo approximation to the marginal algorithm, are also given.