Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
A survey of research in deliberative real-time artificial intelligence
Real-Time Systems
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
Solving Time-Dependent Planning Problems
Solving Time-Dependent Planning Problems
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
Surface roughness statistic models of metallized coatings in grinding manufacturing system
WSEAS TRANSACTIONS on SYSTEMS
Thermal sprayed coatings adherence: influencing parameters
WSEAS Transactions on Systems and Control
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This paper presents a method based on multiple regression models to select algorithms for the inference tasks in Bayesian networks. The method may be applied when exact and approximate schemes are used to perform inferences. Multiple characterizations of Bayesian networks and prediction models are considered to select the algorithm that will provide the least relative error in future inferences. Logistic regression model is applied to determine when exact algorithms may be used for specific tasks. The prediction models of approximate inference algorithms are created by multiple regression analysis, based on simulation data using Variable Elimination, Gibbs Sampling and Stratified Simulation algorithms. Experimental analyses compare some alternative approaches and show better results when multivariate analysis is applied.