Bayesian model learning based on a parallel MCMC strategy
Bayesian model learning based on a parallel MCMC strategy
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DOI：
10.1007/s11222-006-9391-y
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发表时间：
2006-12-01
影响因子：
2.2
通讯作者：
Koski, Timo
中科院分区：
文献类型：
--
作者：
Corander, Jukka;Gyllenberg, Mats;Koski, Timo
提出了一种新的马尔可夫链蒙特卡罗算法，用于估计离散模型空间上的后验概率。我们的学习方法适用于在给定任何固定结构的情况下，边际似然可以解析地计算的模型族，无论是精确的还是近似的。对于某些模型邻域结构，一般的可逆Metropolis-Hastings算法不能给出估计问题的适当解。因此，我们开发了一种替代的、不可逆的算法，该算法可以避免邻域的缩放效应。为了有效地探索模型空间，使用了有限个相互作用的并行随机过程。我们的交互方案允许同时探索模型空间的几个局部邻域，同时它防止将任何特定过程吸收到相对较低的状态。通过对一个分类模型的应用，说明了该方法的优点。特别是，我们使用了一个广泛的细菌数据库，并将我们的结果与相同数据的不同方法获得的结果进行了比较。
We introduce a novel Markov chain Monte Carlo algorithm for estimation of posterior probabilities over discrete model spaces. Our learning approach is applicable to families of models for which the marginal likelihood can be analytically calculated, either exactly or approximately, given any fixed structure. It is argued that for certain model neighborhood structures, the ordinary reversible Metropolis-Hastings algorithm does not yield an appropriate solution to the estimation problem. Therefore, we develop an alternative, non-reversible algorithm which can avoid the scaling effect of the neighborhood. To efficiently explore a model space, a finite number of interacting parallel stochastic processes is utilized. Our interaction scheme enables exploration of several local neighborhoods of a model space simultaneously, while it prevents the absorption of any particular process to a relatively inferior state. We illustrate the advantages of our method by an application to a classification model. In particular, we use an extensive bacterial database and compare our results with results obtained by different methods for the same data.