Independence Semantics for BKBs.

Eugene Santos; Tzachi Rosen; Eyal Shlomo Shimony

Independence Semantics for BKBs.

Eugene Santos, Tzachi Rosen, Eyal Shlomo Shimony

Department of Computer Science

Research output: Contribution to conference › Paper › peer-review

Abstract

Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extend Bayes Networks (BN), by allowing context-sensitive independence and cycles in the directed graph. BKBs have probabilistic seman- tics, but lack independence semantics, i.e., a graph- based scheme determining what independence state- ments are sanctioned by the model. Such a semantics is provided through generalized d- separation, by constructing an equivalent BN. While useful for showing correctness, the construction is not practical for decision algorithms due to exponential size. Some results for special cases, where indepen- dence can be determined from polynomial-time tests on the BKB graph, are presented.

Original language	English
Number of pages	5
State	Published - 1 Jan 2000
Event	Proceedings of the Thirteenth International Florida Artificial Intelligence (FLAIRS) - Orlando, United States Duration: 22 May 2000 → 24 May 2000 https://www.aaai.org/Library/FLAIRS/flairs00contents.php

Conference

Conference	Proceedings of the Thirteenth International Florida Artificial Intelligence (FLAIRS)
Country/Territory	United States
City	Orlando
Period	22/05/00 → 24/05/00
Internet address	https://www.aaai.org/Library/FLAIRS/flairs00contents.php

Cite this

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title = "Independence Semantics for BKBs.",

abstract = "Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extend Bayes Networks (BN), by allowing context-sensitive independence and cycles in the directed graph. BKBs have probabilistic seman- tics, but lack independence semantics, i.e., a graph- based scheme determining what independence state- ments are sanctioned by the model. Such a semantics is provided through generalized d- separation, by constructing an equivalent BN. While useful for showing correctness, the construction is not practical for decision algorithms due to exponential size. Some results for special cases, where indepen- dence can be determined from polynomial-time tests on the BKB graph, are presented.",

author = "Eugene Santos and Tzachi Rosen and Shimony, {Eyal Shlomo}",

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TY - CONF

T1 - Independence Semantics for BKBs.

AU - Santos, Eugene

AU - Rosen, Tzachi

AU - Shimony, Eyal Shlomo

PY - 2000/1/1

Y1 - 2000/1/1

N2 - Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extend Bayes Networks (BN), by allowing context-sensitive independence and cycles in the directed graph. BKBs have probabilistic seman- tics, but lack independence semantics, i.e., a graph- based scheme determining what independence state- ments are sanctioned by the model. Such a semantics is provided through generalized d- separation, by constructing an equivalent BN. While useful for showing correctness, the construction is not practical for decision algorithms due to exponential size. Some results for special cases, where indepen- dence can be determined from polynomial-time tests on the BKB graph, are presented.

AB - Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extend Bayes Networks (BN), by allowing context-sensitive independence and cycles in the directed graph. BKBs have probabilistic seman- tics, but lack independence semantics, i.e., a graph- based scheme determining what independence state- ments are sanctioned by the model. Such a semantics is provided through generalized d- separation, by constructing an equivalent BN. While useful for showing correctness, the construction is not practical for decision algorithms due to exponential size. Some results for special cases, where indepen- dence can be determined from polynomial-time tests on the BKB graph, are presented.

M3 - Paper

T2 - Proceedings of the Thirteenth International Florida Artificial Intelligence (FLAIRS)

Y2 - 22 May 2000 through 24 May 2000

ER -

Independence Semantics for BKBs.

Abstract

Conference

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