Results 1  10
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186
Fusion, Propagation, and Structuring in Belief Networks
 ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
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Cited by 484 (8 self)
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Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used
OntoMerge: A System for Merging DLLite
"... Abstract. Merging multisourced ontologies in a consistent manner is an important and challenging research topic. In this paper, we propose a novel approach for merging DLLite N bool ontologies by adapting the classical modelbased belief merging approach, where the minimality of changes is realise ..."
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Abstract. Merging multisourced ontologies in a consistent manner is an important and challenging research topic. In this paper, we propose a novel approach for merging DLLite N bool ontologies by adapting the classical modelbased belief merging approach, where the minimality of changes
On the Revision of Probabilistic Belief States
 Notre Dame Journal of Formal Logic
, 1995
"... In this paper we describe two approaches to the revision of probability functions. We assume that a probabilistic state of belief is captured by a counterfactual probability or Popper function, the revision of which determines a new Popper function. We describe methods whereby the original function ..."
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Cited by 5 (1 self)
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In this paper we describe two approaches to the revision of probability functions. We assume that a probabilistic state of belief is captured by a counterfactual probability or Popper function, the revision of which determines a new Popper function. We describe methods whereby the original function
Mixture models for optical flow computation”,
 Proc. IEEE Conf. Comput. Vision Pettern Recog.,
, 1993
"... Abstract The computation of optical ow relies on merging information available over an image patch to form an estimate of 2D image velocity a t a p o i n t. This merging process raises a host of issues, which include the treatment of outliers in component v elocity measurements and the modeling of ..."
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Cited by 158 (16 self)
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of multiple motions within a patch which arise from occlusion boundaries or transparency. W e present a new approach which allows us to deal with these issues within a common framework. Our approach is based on the use of a probabilistic mixture m o del to explicitly represent m ultiple motions within a patch
Semantics of ranking queries for probabilistic data and expected ranks
 In Proc. of ICDE’09
, 2009
"... Abstract — When dealing with massive quantities of data, topk queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring function. The problem of efficiently answering such ranking queries has been studied and analyzed extensively within traditi ..."
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Cited by 63 (1 self)
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Abstract — When dealing with massive quantities of data, topk queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring function. The problem of efficiently answering such ranking queries has been studied and analyzed extensively within
A Mathematical Analysis of Tournament Selection
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme used to prefer better individuals. In this paper a new description model for selection schemes is introduced that operates on ..."
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Cited by 84 (2 self)
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Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme used to prefer better individuals. In this paper a new description model for selection schemes is introduced that operates
DempsterShafer theory and statistical inference with weak beliefs
, 2008
"... DempsterShafer (DS) theory is a powerful tool for probabilistic reasoning and decisionmaking based on a formal calculus for combining statistical and nonstatistical evidence, as represented by a system of belief functions. DS theory has been widely used in computer science and engineering applica ..."
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Cited by 22 (16 self)
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DempsterShafer (DS) theory is a powerful tool for probabilistic reasoning and decisionmaking based on a formal calculus for combining statistical and nonstatistical evidence, as represented by a system of belief functions. DS theory has been widely used in computer science and engineering
On probabilistic time versus alternating time
 Electronic Colloquium on Computational Complexity
, 2005
"... We prove several new results regarding the relationship between probabilistic time, BPTime(t), and alternating time, Σ O(1)Time(t). Our main results are the following: 1. We prove that BPTime(t) ⊆ Σ3Time(t · poly log t). Previous results show that BPTime (t) ⊆ Σ2Time � t 2 · log t � (Sipser and Gá ..."
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Cited by 3 (0 self)
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that solving QSAT 3 ∈ Σ3Time(n · poly log n) requires time n 1+Ω(1) on probabilistic Turing machines using space n.9, with random access to input and work tapes, and twoway sequential access to the randombit tape. This is the first lower bound of the form t = n 1+Ω(1) on a model with random access
HUGIN* — a Shell for Building Bayesian Belief Universes for
"... Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN she ..."
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Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN
Fast Construction of Irreducible Polynomials over Finite Fields
 J. Symbolic Comput
, 1993
"... The main result of this paper is a new algorithm for constructing an irreducible polynomial of specified degree n over a finite field F q . The algorithm is probabilistic, and is asymptotically faster than previously known algorithms for this problem. It uses an expected number of O~(n 2 + n log q) ..."
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Cited by 61 (6 self)
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The main result of this paper is a new algorithm for constructing an irreducible polynomial of specified degree n over a finite field F q . The algorithm is probabilistic, and is asymptotically faster than previously known algorithms for this problem. It uses an expected number of O~(n 2 + n log q
Results 1  10
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186