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A Survey of Algorithms for RealTime Bayesian Network Inference
 In In the joint AAAI02/KDD02/UAI02 workshop on RealTime Decision Support and Diagnosis Systems
, 2002
"... As Bayesian networks are applied to more complex and realistic realworld applications, the development of more efficient inference algorithms working under realtime constraints is becoming more and more important. This paper presents a survey of various exact and approximate Bayesian network ..."
Abstract

Cited by 33 (2 self)
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As Bayesian networks are applied to more complex and realistic realworld applications, the development of more efficient inference algorithms working under realtime constraints is becoming more and more important. This paper presents a survey of various exact and approximate Bayesian network inference algorithms. In particular, previous research on realtime inference is reviewed. It provides a framework for understanding these algorithms and the relationships between them. Some important issues in realtime Bayesian networks inference are also discussed.
Algorithm Selection for Sorting and Probabilistic Inference: A Machine LearningBased Approach
 KANSAS STATE UNIVERSITY
, 2003
"... The algorithm selection problem aims at selecting the best algorithm for a given computational problem instance according to some characteristics of the instance. In this dissertation, we first introduce some results from theoretical investigation of the algorithm selection problem. We show, by Rice ..."
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Cited by 8 (0 self)
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The algorithm selection problem aims at selecting the best algorithm for a given computational problem instance according to some characteristics of the instance. In this dissertation, we first introduce some results from theoretical investigation of the algorithm selection problem. We show, by Rice's theorem, the nonexistence of an automatic algorithm selection program based only on the description of the input instance and the competing algorithms. We also describe an abstract theoretical framework of instance hardness and algorithm performance based on Kolmogorov complexity to show that algorithm selection for search is also incomputable. Driven by the theoretical results, we propose a machine learningbased inductive approach using experimental algorithmic methods and machine learning techniques to solve the algorithm selection problem. Experimentally, we have
A Bayesian System for Integration of Algorithms for RealTime Bayesian Network Inference
, 2002
"... Bayesian networks (BNs) are a key method for representation and reasoning under uncertainty in artificial intelligence. Both exact and approximate BN inference have been proven to be NPhard. The problems of inference become even less tractable under realtime constraints. One solution to realtime ..."
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Bayesian networks (BNs) are a key method for representation and reasoning under uncertainty in artificial intelligence. Both exact and approximate BN inference have been proven to be NPhard. The problems of inference become even less tractable under realtime constraints. One solution to realtime AI problems is to develop anytime algorithms. Anytime algorithms are iterative refinement algorithms that trade performance for time. They improve the quality of the output as the amount of time increases. Another solution to realtime AI consists of metareasoning and integrating multiple approximation methods. To date, researchers have developed various exact and approximate BN inference algorithms. Each of these has different properties and works better for different classes of inference problems. Given a BN inference problem instance, it is usually hard but important to decide in advance which algorithm among a set of choices is the most appropriate. This problem is known as the algorithm selection problem. This dissertation proposal addresses the problem of realtime BN inference. It proposes work on both development of new anytime approximation algorithms and integration of multiple inference algorithms. Specifically, I first propose...