(Enter summary)
Abstract: A belief network is a graphical representation of the underlying probabilistic relationships
in a complex system. Belief networks have been employed as a representation
of uncertain relationships in computer-based diagnostic systems. These diagnostic systems
provide assistance by assigning likelihoods to alternative explanatory hypotheses
in response to a set of findings or observations. Approximation algorithms have been
used to compute likelihoods of hypotheses in large networks. We... (Update)
Context of citations to this paper: More
.... include straight simulation [25, 26] forward simulation, 15] likelihood weighting [13, 33] and randomized approximation schemes [3, 4, 7, 8]. Variants of these methods such as backward simulation [14] exist; Neal [23] provides a good overview of the theory of simulation...
...a simulation of the corresponding Bayesian network. We mention the algorithms introduced by Chavez and Cooper [3] Dagum and Horvitz [6], Fung and Chang [12] Henrion [17] Hryceij [20] Jensen et al. 21] Pearl [36] Shachter and Peot [43] and Shwe and Cooper [44] 4 Our...
Cited by: More
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Modeling Decisions in Dynamic Domains - Summary In Many
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Optimal Monte Carlo Estimation of Belief Network Inference - Pradhan, Dagum (1996)
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12: Approximating probabilistic inference in Bayesian belief networks is NP-hard (context) - Dagum, Luby - 1993
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8: Local computations with probabilities on graphical structures and their applicat.. (context) - Lauritzen, Spiegelhalter - 1988
BibTeX entry: (Update)
P. Dagum and E. Horvitz. A Bayesian Analysis of Simulation Algorithms for Inference in Belief Networks. Networks, 23:499-516, 1993. http://citeseer.ist.psu.edu/dagum93bayesian.html More
@misc{ dagum93bayesian,
author = "P. Dagum and E. Horvitz",
title = "A Bayesian Analysis of Simulation Algorithms for Inference in Belief Networks",
text = "P. Dagum and E. Horvitz. A Bayesian Analysis of Simulation Algorithms for
Inference in Belief Networks. Networks, 23:499-516, 1993.",
year = "1993",
url = "citeseer.ist.psu.edu/dagum93bayesian.html" }
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