Sequential Monte Carlo samplers
Sequential Monte Carlo samplers
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DOI：
10.1111/j.1467-9868.2006.00553.x
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发表时间：
2006-01-01
影响因子：
5.8
通讯作者：
Jasra, Ajay
中科院分区：
文献类型：
--
作者：
Del Moral, Pierre;Doucet, Arnaud;Jasra, Ajay
我们提出了一种方法，从定义在一个共同的空间，每个分布被称为一个归一化常数的概率分布序列顺序采样。这些概率分布近似于云的加权随机样本随时间的推移，通过使用顺序蒙特卡罗方法传播。这种方法使我们能够推导出简单的算法，使并行马尔可夫链蒙特卡罗算法相互作用，以执行全局优化和顺序贝叶斯估计，并计算归一化常数的比率。我们说明了这些算法的贝叶斯推理的背景下产生的各种集成任务。
We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global optimization and sequential Bayesian estimation and to compute ratios of normalizing constants. We illustrate these algorithms for various integration tasks arising in the context of Bayesian inference.