In this paper, we put forth the first join tree propagation algorithm that selectively applies either arc reversal (AR) or variable elimination (VE) to build the propagated messages. Our approach utilizes a recent method for identifying the propagated join tree messagesà priori. When it is determined that precisely one message is to be constructed at a join tree node, VE is utilized to build this distribution; otherwise, AR is applied as it is better suited to construct multiple distributions passed between neighboring join tree nodes. Experimental results, involving evidence processing in seven real-world and one benchmark Bayesian network, empirically demonstrate that selectively applying VE and AR is faster than applying one of these methods exclusively on the entire network.

Current join tree propagation algorithms treat all propagated messages as being of equal importance. On the contrary, it is often the case in real-world Bayesian networks that only some of the messages propagated from one join tree node to another are relevant to subsequent message construction at the receiving node. In this article, we propose the first join tree propagation algorithm that identifies and constructs the relevant messages first. Our approach assigns lower priority to the irrelevant messages as they only need to be constructed so that posterior probabilities can be computed when propagation terminates. Experimental results, involving the processing of evidence in four real-world Bayesian networks, empirically demonstrate an improvement over the stateof-the-art method for exact inference in discrete Bayesian networks.

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Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, currently including viewing data as a set of records, attributes, frequencies, intervals, sequences, or sequences of changes. By using these six views of the data, multiple categories of peculiar data can be detected to reveal different aspects of the data. For each view, the framework provides an extensible set of peculiarity measures to detect outliers and other kinds of peculiar data. The PDD framework has been implemented for Oracle and Access. Experiments are reported for data sets concerning Regina weather and NHL hockey.

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