From Statistics to Beliefs

From Statistics to Beliefs (1992)

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by Fahiem Bacchus , Adam Grove , Joseph Y. Halpern , Daphne Koller

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Datum	Value	Source
TITLE	From Statistics to Beliefs	user correction - Legacy Corrections
AUTHOR NAME	Fahiem Bacchus	user correction
AUTHOR AFFIL	Computer Science Dept., University of Waterloo	user correction
AUTHOR ADDR	Waterloo, Ontario, Canada, N2L 3G1	user correction
AUTHOR NAME	Adam Grove	user correction
AUTHOR AFFIL	Computer Science Dept., Stanford University	user correction
AUTHOR ADDR	Stanford, CA 943005	user correction
AUTHOR NAME	Joseph Y. Halpern	user correction
AUTHOR AFFIL	IBM Almaden Research Center	user correction
AUTHOR ADDR	650 Harry Road; San Jose, CA 95120--6099	user correction
AUTHOR NAME	Daphne Koller	user correction
AUTHOR AFFIL	Computer Science Dept., Stanford University	user correction
AUTHOR ADDR	Stanford, CA 943005	user correction
ABSTRACT	An intelligent agent uses known facts, including statistical knowledge, to assign degrees of belief to assertions it is uncertain about. We investigate three principled techniques for doing this. All three are applications of the principle of indifference, because they assign equal degree of belief to all basic "situations " consistent with the knowledge base. They differ because there are competing intuitions about what the basic situations are. Various natural patterns of reasoning, such as the preference for the most specific statistical data available, turn out to follow from some or all of the techniques. This is an improvement over earlier theories, such as work on direct inference and reference classes, which arbitrarily postulate these patterns without offering any deeper explanations or guarantees of consistency. The three methods we investigate have surprising characterizations: there are connections to the principle of maximum entropy, a principle of maximal independence, an...	user correction - Legacy Corrections
YEAR	1992	user correction - Legacy Corrections
CITATIONS	21 found	ParsCit 1.0