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Bayesian Network Classifiers

Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
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Context-Specific Independence in Bayesian Networks

This paper proposes a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node, and proposes a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network.
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Stochastic dynamic programming with factored representations

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Learning Bayesian Networks with Local Structure

A novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks and indicates that learning curves characterizing the procedure that exploits the local structure converge faster than these of the standard procedure.
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Exploiting Structure in Policy Construction

This work presents an algorithm, called structured policy Iteration (SPI), that constructs optimal policies without explicit enumeration of the state space, and retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but exploits the variable and prepositional independencies reflected in a temporal Bayesian network representation of MDPs.
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Data Analysis with Bayesian Networks: A Bootstrap Approach

This paper proposes Efron's Bootstrap as a computationally efficient approach for answering confidence measures on features of Bayesian networks, and proposes to use these confidence measures to induce better structures from the data, and to detect the presence of latent variables.
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Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control

Experimental results from a testbed show that TAN models involving small subsets of metrics capture patterns of performance behavior in a way that is accurate and yields insights into the causes of observed performance effects.
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Capturing, indexing, clustering, and retrieving system history

We present a method for automatically extracting from a running system an indexable signature that distills the essential characteristic from a system state and that can be subjected to automated…
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Fingerprinting the datacenter: automated classification of performance crises

This work proposes and evaluates a methodology for automatic classification and identification of crises, and in particular for detecting whether a given crisis has been seen before, so that a known solution may be immediately applied.
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Discretizing Continuous Attributes While Learning Bayesian Networks

A method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process is introduced, using a new metric based on the Minimal Description Length principle for choosing the threshold values for theDiscretization while learning the Bayesian network structure.
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