Parallel exact inference on the cell broadband engine processor
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Parallell interacting MCMC for learning of topologies of graphical models
Data Mining and Knowledge Discovery
Bayesian unsupervised learning of DNA regulatory binding regions
Advances in Artificial Intelligence
Parallel exact inference on the Cell Broadband Engine processor
Journal of Parallel and Distributed Computing
Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm
Computational Statistics & Data Analysis
Have I seen you before? Principles of Bayesian predictive classification revisited
Statistics and Computing
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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.