Experiments in stochastic computation for high-dimensional graphical models
Experiments in stochastic computation for high-dimensional graphical models
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DOI：
10.1214/088342305000000304
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发表时间：
2005-11-01
影响因子：
5.7
通讯作者：
West, M
中科院分区：
文献类型：
--
作者：
Jones, B;Carvalho, C;West, M
我们讨论了高斯图形模型中随机计算方法的实现、发展和性能。我们从高维模型搜索的角度来看待这些方法，特别关注马尔可夫链蒙特卡罗（MCMC）和其他随机搜索方法的可扩展性。在回顾了无向高斯图形模型的结构和背景以及模型的不确定性（协方差选择）之后，我们讨论了先验规范，包括模型上的新先验，然后使用各种随机计算方法探索了一些例子。传统的MCMC方法的出发点，本实验，然后我们开发替代随机搜索的想法和对比这种新的方法与MCMC。我们的例子范围从低（12-20）到中等（150）维，并结合联合收割机简单的合成的例子与基因表达研究的数据分析。最后，我们的意见的需要和潜力，新的计算方法在更高的维度，包括建设性的方法高斯图形建模和计算。
We discuss the implementation, development and performance of methods of stochastic computation in Gaussian graphical models. We view these methods from the perspective of high-dimensional model search, with a particular interest in the scalability with dimension of Markov chain Monte Carlo (MCMC) and other stochastic search methods. After reviewing the structure and context of undirected Gaussian graphical models and model uncertainty (covariance selection), we discuss prior specifications, including new priors over models, and then explore a number of examples using various methods of stochastic computation. Traditional MCMC methods are the point of departure for this experimentation; we then develop alternative stochastic search ideas and contrast this new approach with MCMC. Our examples range from low (12-20) to moderate (150) dimension, and combine simple synthetic examples with data analysis from gene expression studies. We conclude with comments about the need and potential for new computational methods in far higher dimensions, including constructive approaches to Gaussian graphical modeling and computation.