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Using Maximum Entropy to compute marginal probabilities in a causal binary tree need not take exponential time

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Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 946))

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Abstract

In a previous paper, the present authors have argued that Maximum Entropy is worth pursuing as a technique for reasoning under uncertainty when information is missing. The main drawback is that maximising Entropy has been shown to be an NP-Complete problem. However, a Maximum Entropy approach to probabilistic reasoning is not necessarily exponentially large to compute in at least one case. This paper shows that given a tree of incomplete causal information (eg. as used by Pearl but with some of the information missing), the probability of the marginals can be found in linear space and time using Maximum Entropy.

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Authors and Affiliations

  1. Dept. of Computing, University of Bradford, BD7 1DP, Bradford, UK

    Gerald R. Garside

Authors
  1. Paul C. Rhodes
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  2. Gerald R. Garside
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Christine Froidevaux Jürg Kohlas

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© 1995 Springer-Verlag Berlin Heidelberg

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Rhodes, P.C., Garside, G.R. (1995). Using Maximum Entropy to compute marginal probabilities in a causal binary tree need not take exponential time. In: Froidevaux, C., Kohlas, J. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1995. Lecture Notes in Computer Science, vol 946. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60112-0_41

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  • DOI: https://doi.org/10.1007/3-540-60112-0_41

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60112-8

  • Online ISBN: 978-3-540-49438-6

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