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Computing Probability Intervals Under Independency Constraints
TL;DR: In this paper, a method for computing probability intervals for probabilities of interest from a partial specification of a joint probability distribution is presented, allowing for independency relationships between statistical variables to be exploited.
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Abstract: Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in knowledge-based systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing such probability intervals for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by allowing for independency relationships between statistical variables to be exploited.
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Citations
Different methods are needed to propagate ignorance and variability
Scott Ferson,Lev R. Ginzburg +1 more
TL;DR: It is argued that the two kinds of uncertainty should be propagated through mathematical expressions with different calculation methods, basically, interval analysis should be used to propagate ignorance, and probability theory should beused to propagate variability.
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Measures of uncertainty in expert systems
TL;DR: Each of the four measures seems to be useful in special kinds of problems, but only lower and upper previsions appear to be sufficiently general to model the most common types of uncertainty.
448
Credal networks
Fabio Gagliardi Cozman
- 01 Jul 2000
TL;DR: Credal networks as mentioned in this paper is a compact representation for a set of probability distributions, and it is closely related to very popular statistical models such as Markov chains, Bayesian networks, Markov random fields, etc.
410
Anytime deduction for probabilistic logic
Alan M. Frisch,Peter Haddawy +1 more
TL;DR: The deduction method presented here contrasts with other methods whose ability to perform logical reasoning is either limited or requires finding all truth assignments consistent with the given sentences.
139
Probabilistic deduction with conditional constraints over basic events
TL;DR: In this paper, the problem of probabilistic deduction with conditional constraints over basic events is shown to be NP-hard, and a local approach is proposed to solve it in polynomial time in the size of the conditional constraint trees.
References
Combinatorial optimization:Algorithms and complexity
TL;DR: Eventually, you will unquestionably discover a supplementary experience and achievement by Spending more cash by spending more cash.
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Probabilistic logic
Nils J. Nilsson
- 01 Feb 1986
TL;DR: In this paper, a semantical generalization of logic in which the truth values of sentences are probabilistic values (between 0 and 1) is presented, which applies to any logical system for which the consistency of a finite set of sentences can be established.
1.3K