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Axiomatizing NoisyOR
 In Proceedings of the 16th European Conference on Artificial Intelligence (ECAI04
, 2004
"... The NoisyOR function is extensively used in probabilistic reasoning, and usually justified with heuristic arguments. This paper investigates sets of conditions that imply the NoisyOR function. 1 ..."
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Cited by 8 (1 self)
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The NoisyOR function is extensively used in probabilistic reasoning, and usually justified with heuristic arguments. This paper investigates sets of conditions that imply the NoisyOR function. 1
Independence for Full Conditional Measures, Graphoids and Bayesian Networks
, 2007
"... This paper examines definitions of independence for events and variables in the context of full conditional measures; that is, when conditional probability is a primitive notion and conditioning is allowed on null events. Several independence concepts are evaluated with respect to graphoid propertie ..."
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Cited by 4 (2 self)
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This paper examines definitions of independence for events and variables in the context of full conditional measures; that is, when conditional probability is a primitive notion and conditioning is allowed on null events. Several independence concepts are evaluated with respect to graphoid properties; we show that properties of weak union, contraction and intersection may fail when null events are present. We propose a concept of “full” independence, characterize the form of a full conditional measure under full independence, and suggest how to build a theory of Bayesian networks that accommodates null events.
Probabilistic Logic with Independence
"... This paper investigates probabilistic logics endowed with independence relations. We review propositional probabilistic languages without and with independence. We then consider graphtheoretic representations for propositional probabilistic logic with independence; complexity is analyzed, algorithm ..."
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This paper investigates probabilistic logics endowed with independence relations. We review propositional probabilistic languages without and with independence. We then consider graphtheoretic representations for propositional probabilistic logic with independence; complexity is analyzed, algorithms are derived, and examples are discussed. Finally, we examine a restricted rstorder probabilistic logic that generalizes relational Bayesian networks.
Computing Lower and Upper Expectations under Epistemic Independence Abstract
"... This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among ..."
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This papers investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among random variables. Separation properties of graphical models are also investigated. Key words: Sets of probability measures, concepts of independence, imprecise probabilities, epistemic independence, multilinear programming 1
Bruno de Finetti and Imprecision: Imprecise Probability Does not Exist!
, 2012
"... We review several of de Finetti’s fundamental contributions where these have played and continue to play an important role in the development of imprecise probability research. Also, we discuss de Finetti’s few, but mostly critical remarks about the prospects for a theory of imprecise probabilities, ..."
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We review several of de Finetti’s fundamental contributions where these have played and continue to play an important role in the development of imprecise probability research. Also, we discuss de Finetti’s few, but mostly critical remarks about the prospects for a theory of imprecise probabilities, given the limited development of imprecise probability theory as that was known to him.