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Reasoning About Belief Uncertainty in
"... Abstract. Dealing with uncertainty is a very important issue in description logics (DLs). In this paper, we present ..."
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Abstract. Dealing with uncertainty is a very important issue in description logics (DLs). In this paper, we present
Preference and Belief: Ambiguity and Competence in Choice under Uncertainty
 JOURNAL OF RISK AND UNCERTAINTY
, 1991
"... We investigate the relation between judgments of probability and preferences between bets. A series of experiments provides support for the competence hypothesis that people prefer betting on their own judgment over an equiprobable chance event when they consider themselves knowledgeable, but not o ..."
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Cited by 305 (6 self)
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of inferring beliefs from preferences is questioned. The uncertainty we encounter in the world is not readily quantified. We may feel that our favorite football team has a good chance to win the championship match, that the price of gold will probably go up, and that the incumbent mayor is unlikely
Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment
 Psychological Review
, 1983
"... Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents, P(A) and.P(B), because the extension (or the possibility set) of the conjunction is included in the exten ..."
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Cited by 461 (6 self)
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are discussed and attempts to combat it are explored. Uncertainty is an unavoidable aspect of the the last decade (see, e.g., Einhorn & Hogarth, human condition. Many significant choices must be based on beliefs about the likelihood
Learning Bayesian belief networks: An approach based on the MDL principle
 Computational Intelligence
, 1994
"... A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being lear ..."
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Cited by 254 (7 self)
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A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being
Reasoning about beliefs and actions under computational resource constraints
 in Proceedings of the 1989 Workshop on Uncertainty and AI
, 1987
"... Although many investigators arm a desire to build reasoning systems that behave consistently with the axiomatic basis dened by probability theory and utility theory, limited resources for engineering and computation can make a complete normative analysis impossible. We attempt to move discussion be ..."
Uncertainty, Belief, and Probability
 Computational Intelligence
, 1989
"... : We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and ..."
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Cited by 52 (2 self)
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and outer measure of the event. In addition to removing the requirement that every event be assigned a probability, our approach circumvents other criticisms of probabilitybased approaches to uncertainty. For example, the measure of belief in an event turns out to be represented by an interval (defined
Inference in belief networks: A procedural guide
 International Journal of Approximate Reasoning
, 1996
"... Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference onbelief networks is the Probability Propagation in Trees of Clusters (PPTC) al ..."
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Cited by 176 (5 self)
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Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference onbelief networks is the Probability Propagation in Trees of Clusters (PPTC
BELIEFS
, 1991
"... In existing models of decision under uncertainty where the decision maker may have multiple beliefs (vague beliefs) the different beliefs are not distinguished by their degree of reliability. This paper extends the model of vague beliefs in this direction: the decision maker may have multiple belief ..."
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In existing models of decision under uncertainty where the decision maker may have multiple beliefs (vague beliefs) the different beliefs are not distinguished by their degree of reliability. This paper extends the model of vague beliefs in this direction: the decision maker may have multiple
Fragile Beliefs and the Price of Uncertainty.
 Quantitative Economics
, 2010
"... A representative consumer uses Bayes' law to learn about parameters of several models and to construct probabilities with which to perform ongoing model averaging. The arrival of signals induces the consumer to alter his posterior distribution over models and parameters. The consumer's sp ..."
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Cited by 28 (4 self)
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A representative consumer uses Bayes' law to learn about parameters of several models and to construct probabilities with which to perform ongoing model averaging. The arrival of signals induces the consumer to alter his posterior distribution over models and parameters. The consumer's specification doubts induce him to slant probabilities pessimistically. The pessimistic probabilities tilt toward a model that puts longrun risks into consumption growth. That contributes a countercyclical historydependent component to prices of risk.
Theory Refinement on Bayesian Networks
, 1991
"... Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced ..."
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Cited by 255 (5 self)
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Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem
Results 1  10
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