Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 8th European Confere Lluís Godo Conference proceedings 2005 Springer-Ve

overweight

Constrained Score+(Local)Search Methods for Learning Bayesian Networksate network to the data, and a search procedure, that explores the space of possible solutions. The most used method inside this family is (iterated) hill climbing, because its good trade-off between CPU requirements, accuracy of the obtained model, and ease of implemetation. In this paper we focus

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Mnemonics

Foundation for the New Algorithm Learning Pseudo-Independent Modelsn this type of domain models, a method called the multiple-link lookahead search is needed. An improved result can be obtained by incorporating model complexity into a scoring metric to explicitly trade off model accuracy for complexity and vice versa during selection of the best model among candida

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Optimal Threshold Policies for Operation of a Dedicated-Platform with Imperfect State Information – edicated-platform has two modes of action at each period of time: it can attempt to process the target-task at the given period of time, or suspend the target-task for later completion. We formulate the optimal trade-off between the processing cost and the latency in completion of the target-task as

原谅

APPSSAT: Approximate Probabilistic Planning Using Stochastic Satisfiability problem to a stochastic satisfiability (.) problem and solving that problem instead [1]. The values of some of the variables in an . instance are probabilistically determined; . considers the most likely instantiations of these variables (the most probable situations facing the agent) and attempts

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Nonlinear Deterministic Relationships in Bayesian Networksnally deterministic variables are developed. We perform inference in networks with nonlinear deterministic variables and non-Gaussian continuous variables by using piecewise linear approximations to nonlinear functions and modeling probability distributions with mixtures of truncated exponentials (MTE) potentials.

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Probabilistic Graphical Models for the Diagnosis of Analog Electrical Circuitsts and their tolerances as well as measurements made on the circuit. Faulty components can be identified by looking for high probabilities for values of characteristic magnitudes that deviate from the nominal values.