Valuation-Based Systems for Bayesian Decision Analysis

Valuation-Based Systems for Bayesian Decision Analysis

This paper proposes a new method for representing and solving Bayesian decison 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, valuatio...

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Veröffentlicht in:Operations Research. - Institute for Operations Research and the Management Sciences, 1956. - 40(1992), 3, Seite 463-484
1. Verfasser: Shenoy, Prakash P. (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 1992
Zugriff auf das übergeordnete Werk:Operations Research
Schlagworte:Decision analysis, theory: representation, and solution using local computation Dynamic programming, Markov, finite state: solution using local computation Networks/graphs: representation for decision, optimization and probabilistic inference problems Decision Analysis, Bargaining and Negotiation Mathematics Economics Behavioral sciences Social sciences Physical sciences
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520 |a This paper proposes a new method for representing and solving Bayesian decison 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. 
540 |a Copyright 1992 The Operations Research Society of America 
650 4 |a Decision analysis, theory: representation, and solution using local computation 
650 4 |a Dynamic programming, Markov, finite state: solution using local computation 
650 4 |a Networks/graphs: representation for decision, optimization and probabilistic inference problems 
650 4 |a Decision Analysis, Bargaining and Negotiation 
650 4 |a Mathematics  |x Pure mathematics  |x Probability theory  |x Random variables 
650 4 |a Economics  |x Economic research  |x Economic analysis  |x Economic value  |x Valuation 
650 4 |a Behavioral sciences  |x Psychology  |x Cognitive psychology  |x Cognitive processes  |x Decision making  |x Decision analysis  |x Decision trees 
650 4 |a Social sciences  |x Communications  |x Communications media  |x Visual materials  |x Graphics  |x Diagrams 
650 4 |a Mathematics  |x Pure mathematics  |x Probability theory  |x Probabilities  |x Conditional probabilities 
650 4 |a Behavioral sciences  |x Human behavior  |x Social behavior  |x Marginalization 
650 4 |a Physical sciences  |x Earth sciences  |x Geophysics  |x Seismology  |x Seismic testing 
650 4 |a Mathematics  |x Applied mathematics  |x Statistics  |x Applied statistics  |x Descriptive statistics  |x Statistical distributions  |x Distribution functions  |x Probability distributions 
650 4 |a Behavioral sciences  |x Psychology  |x Cognitive psychology  |x Cognitive processes  |x Decision making  |x Decision analysis 
650 4 |a Mathematics  |x Mathematical procedures  |x Factorization 
655 4 |a research-article 
773 0 8 |i Enthalten in  |t Operations Research  |d Institute for Operations Research and the Management Sciences, 1956  |g 40(1992), 3, Seite 463-484  |w (DE-627)320595005  |w (DE-600)2019440-7  |x 15265463  |7 nnns 
773 1 8 |g volume:40  |g year:1992  |g number:3  |g pages:463-484 
856 4 0 |u https://www.jstor.org/stable/171411  |3 Volltext 
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952 |d 40  |j 1992  |e 3  |h 463-484