Population Markov Chain Monte Carlo
Population Markov Chain Monte Carlo
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DOI：
10.1023/a:1020206129842
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发表时间：
2003-01-01
期刊：
影响因子：
7.5
通讯作者：
Myers, JW
中科院分区：
文献类型：
--
作者：
Laskey, KB;Myers, JW
受物理和生物系统启发的随机搜索算法被应用于在存在缺失观测值和隐藏变量的情况下学习有向图形概率模型的问题。对于这类问题，确定性搜索算法往往会在局部最优处停止，需要随机重新启动以获得可接受质量的解。我们比较了三种随机搜索算法：Metropolis-Hastings Sampler (MHS), Evolutionary Algorithm （EA）和一种新的混合算法，称为Population Markov Chain Monte Carlo （popMCMC）。PopMCMC使用来自mhs总体的统计信息来告知总体中单个采样者的建议分布。实验结果表明，与没有信息交换的MHS相比，popMCMC和ea的学习效率更高。MCMC样本种群比根据不满足物理启发的局部可逆性条件的ea进化的种群表现出更大的多样性。
Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a Metropolis-Hastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physics-inspired local reversibility conditions.