Chao-Lin Liu and Michael Wellman. On state-space abstraction for anytime evaluation of Bayesian networks. SIGART Bulletin, 7(2):50--57, 1996. Special issue on Anytime Algorithms and Deliberation Scheduling. 13 Home/Search Document Details and Download
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This paper is cited in the following contexts:
Belief Network Inference Algorithms: a Study of Performance.. - Jitnah, Nicholson (1996) (3 citations) (Correct)
....nodes and of their children are recomputed. For each state of a child of an abstracted node, the new conditional probability is assigned as an sum of the original conditional probabilities weighted by the prior probability of the abstracted states. No empirical performance results are given. In [28, 21], Wellman and Liu use the same technique of state space abstraction, but instead they set the new conditional probabilities of children of an abstracted node as an average of the original conditional probabilities. This version of state space abstraction is tested on a set of example networks and ....
C. Liu and M. Wellman. On state-space abstraction for anytime evaluation of bayesian networks. In IJCAI 95: Anytime Algorithms and Deliberation Scheduling Workshop, pages 91--98, 1995.
Efficient Reasoning - Greiner, Darken, Santoso (1998) (1 citation) (Correct)
....(the correct) B to compute this conditional proability. Here, BNr6 Ns is the network obtained by removing the N r N s arc from the network B, PNr6 Ns ( H j E ) be the probability value returned by this network, and P( H j E ) be the result obtained by the original network. Wellman and Liu [LW97, Wel94] leave the structure of the belief net unaffected; their algorithm reduces the complexity of inference by abstracting the state of some individual variables; e.g. changing a variable that could range over 10 states to one that could only range over say 4 values, by partitioning the values of the ....
C.-L. Liu and M. P. Wellman. On state-space abstraction for anytime evaluation of bayesian network. Sigart Bulletin, 7(2), 1997.
Belief Network Algorithms: a Study of Performance Using.. - Jitnah, Nicholson (1996) (2 citations) (Correct)
....improvement in the accuracy of updated beliefs. Model Approximation Another approach to complexity reduction is to approximate the model by preprocessing the network. Methods have been proposed which do this by one of the following methods: removal of weak links [10] state space abstraction [12], replacing small probabilities with zero, graph pruning and node merging [2] These procedures may be applied individually or in combination. It is possible to use such procedures as anytime algorithms to obtain results that improve as more time, memory or information are available. For example ....
C. Liu and M. Wellman. On state-space abstraction for anytime evaluation of bayesian networks. In IJCAI 95: Anytime Algorithms and Deliberation Scheduling Workshop, pages 91--98, 1995.
Using Mutual Information to determine Relevance in Bayesian.. - Nicholson And (1998) (1 citation) (Correct)
....arc weights to search for the best nodes to include in the active set in Localized Partial Evaluation. In [9] we describe a framework for anytime evaluation of a BN by best first traversal, using arc weights based on the Bhattacharyya measure as part of the search criterion. Wellman and Liu in [12] suggest that a measure of informativeness be used when selecting nodes to coarsen or refine in State Space Abstraction, which is another technique for anytime BN evaluation. The weights based on MI presented in this paper are suitable for all these purposes. The complexity of the calculation of ....
....we have yet to provide formal analysis of the relationship between weights based on mutual information and the error. We are also investigating the performance of these weights with existing techniques for anytime BN updating, such as best first traversal [10, 9] and state space abstraction [12], and also with the jointree evaluation algorithm. ....
C. Liu and M. Wellman. On state-space abstraction for anytime evaluation of bayesian networks. In IJCAI 95: Anytime Algorithms and Deliberation Scheduling Workshop, pages 91--98, 1995.
Finding Equilibria in Large Sequential Games of - Imperfect Information Andrew (2005) (Correct)
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Chao-Lin Liu and Michael Wellman. On state-space abstraction for anytime evaluation of Bayesian networks. SIGART Bulletin, 7(2):50--57, 1996. Special issue on Anytime Algorithms and Deliberation Scheduling. 13
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