The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Prakash P. Shenoy - One of the best experts on this subject based on the ideXlab platform.
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A Fusion Algorithm for Solving Bayesian Decision Problems
arXiv: Artificial Intelligence, 2013Co-Authors: Prakash P. ShenoyAbstract:This paper proposes a new method for solving Bayesian Decision problems. The method consists of representing a Bayesian Decision problem as a valuation-based system and applying a fusion algorithm for solving it. The fusion algorithm is a hybrid of local computational methods for computation of marginals of joint probability distributions and the local computational methods for discrete optimization problems.
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valuation based systems for Bayesian Decision analysis
Operations Research, 1992Co-Authors: Prakash P. ShenoyAbstract:This paper proposes a new method for representing and solving Bayesian Decision problems. The representation is called a valuation-based system and has some similarities to influence diagrams. However, unlike influence diagrams which emphasize conditional independence among random variables, valuation-based systems emphasize factorizations of joint probability distributions. Also, whereas influence diagram representation allows only conditional probabilities, valuation-based system representation allows all probabilities. The solution method is a hybrid of local computational methods for the computation of marginals of joint probability distributions and the local computational methods for discrete optimization problems. We briefly compare our representation and solution methods to those of influence diagrams.
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UAI - A fusion algorithm for solving Bayesian Decision problems
Uncertainty Proceedings 1991, 1991Co-Authors: Prakash P. ShenoyAbstract:This paper proposes a new method for solving Bayesian Decision problems. The method consists of representing a Bayesian Decision problem as a valuation-based system and applying a fusion algorithm for solving it. The fusion algorithm is a hybrid of local computational methods for computation of marginals of joint probability distributions and the local computational methods for discrete optimization problems.
Finn Verner Jensen - One of the best experts on this subject based on the ideXlab platform.
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Lazy Evaluation of Symmetric Bayesian Decision Problems
arXiv: Artificial Intelligence, 2013Co-Authors: Anders L. Madsen, Finn Verner JensenAbstract:Solving symmetric Bayesian Decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian Decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the hugin and valuation-based systems architectures for solving symmetric Bayesian Decision problems.
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UAI - Lazy evaluation of symmetric Bayesian Decision problems
1999Co-Authors: Anders L. Madsen, Finn Verner JensenAbstract:Solving symmetric Bayesian Decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian Decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the HUGIN and valuation-based systems architectures for solving symmetric Bayesian Decision problems.
Anders L. Madsen - One of the best experts on this subject based on the ideXlab platform.
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Lazy Evaluation of Symmetric Bayesian Decision Problems
arXiv: Artificial Intelligence, 2013Co-Authors: Anders L. Madsen, Finn Verner JensenAbstract:Solving symmetric Bayesian Decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian Decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the hugin and valuation-based systems architectures for solving symmetric Bayesian Decision problems.
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Practical modeling of Bayesian Decision problems - exploiting deterministic relations
IEEE transactions on systems man and cybernetics. Part B Cybernetics : a publication of the IEEE Systems Man and Cybernetics Society, 2002Co-Authors: Anders L. Madsen, Kristian G. Olesen, Søren L. DittmerAbstract:The widespread use of influence diagrams to represent and solve Bayesian Decision problems is still limited by the inflexibility and rather restrictive semantics of influence diagrams. We propose a number of extensions and adjustments to the definition of influence diagrams in order to make the practical use of influence diagrams more flexible and less restrictive. In particular, we describe how deterministic relations can be exploited to increase the flexibility and efficiency of representing and solving Bayesian Decision problems. The issues addressed in the paper were motivated by the construction of a Decision support system for mission management of unmanned underwater vehicles (UUVs).
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UAI - Lazy evaluation of symmetric Bayesian Decision problems
1999Co-Authors: Anders L. Madsen, Finn Verner JensenAbstract:Solving symmetric Bayesian Decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian Decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the HUGIN and valuation-based systems architectures for solving symmetric Bayesian Decision problems.
Hendrik J. Vos - One of the best experts on this subject based on the ideXlab platform.
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Applications of Bayesian Decision theory to intelligent tutoring systems
Computers in Human Behavior, 1995Co-Authors: Hendrik J. VosAbstract:The purpose of this paper is to consider some applications of Bayesian Decision theory to intelligent tutoring systems. In particular, it will be indicated how the problem of adapting the appropriate amount of instruction to the changing nature of student's capabilities during the learning process can be situated within the general framework of Bayesian Decision theory. Two basic elements of this approach will be used to improve instructional Decision making in intelligent tutoring systems. First, it is argued that in many Decision-making situations the linear loss model is a realistic representation of the losses actually incurred. Second, it is shown that the psychometric model relating observed test scores to the true level of functioning can be represented by Kelley's regression line from classical test theory. Optimal Decision rules will be derived using these two features.
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Applications of Bayesian Decision theory to intelligent tutoring systems
1994Co-Authors: Hendrik J. VosAbstract:Some applications of Bayesian Decision theory to intelligent tutoring systems are considered. How the problem of adapting the appropriate amount of instruction to the changing nature of a student's capabilities during the learning process can be situated in the general framework of Bayesian Decision theory is discussed in the context of the Minnesota Adaptive Instructional System (MAIS). Two basic elements of this approach are used to improve instructional Decision making in intelligent tutoring systems. First, it is argued that in many Decision-making situations the linear loss model is a realistic representation of the losses actually incurred. Second, it is shown that the psychometric model relating observed test scores to the true level of functioning can be represented by Kelley's regression line from classical test theory. Optimal Decision rules for the MAIS are derived using these two features.
Raehong Park - One of the best experts on this subject based on the ideXlab platform.
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fast cu partitioning algorithm for hevc using an online learning based Bayesian Decision rule
IEEE Transactions on Circuits and Systems for Video Technology, 2016Co-Authors: Hyosong Kim, Raehong ParkAbstract:High Efficiency Video Coding (HEVC) is the state-of-the-art video coding standard. It adopts a hierarchical quad-tree-based coding unit (CU) partitioning structure that is flexible in various texture and motion characteristics of a video signal. However, the exhaustive partitioning process for finding optimal CU partitions requires a dramatic increase in computational complexity of the HEVC encoder compared with previous video coding standards. In this paper, a fast CU partitioning algorithm is proposed for HEVC encoder, which early on terminates the CU partitioning process based on the Bayesian Decision rule using joint online and offline learning. An online learning method is first presented based on the minimum error Bayesian Decision rule using a training picture selection method with scene change detection. Next, a joint online and offline learning method is presented, which additionally trains the loss of Decision making of the proposed method based on the minimum risk Bayesian Decision rule. The proposed method is implemented on an HEVC test software 15.0. Experimental results show that the proposed method reduces the computational complexity of HEVC encoder to 53.6% on an average with a 0.71% acceptable Bjontegaard delta bitrate loss in random access configuration. For other configurations, 48.4%, 48.5%, and 54.2% encoding time saving are obtained on an average for low delay, low delay-P, and all intra-configurations, respectively.
