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Probabilistic Extensions of Process Algebras
 Handbook of Process Algebra
, 2001
"... INTRODUCTION Classic process, algebras such as CCS, CSP and ACP, are wellestablished techniques for modelling and reasoning about functional aspects of concurrent processes. The motivation for studying probabilistic extensions of process algebras is to develop techniques dealing with nonfunctiona ..."
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Cited by 80 (7 self)
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INTRODUCTION Classic process, algebras such as CCS, CSP and ACP, are wellestablished techniques for modelling and reasoning about functional aspects of concurrent processes. The motivation for studying probabilistic extensions of process algebras is to develop techniques dealing with non
A probabilistic extension to ontology language owl
 In Proceedings of the 37th Hawaii International Conference On System Sciences (HICSS37), Big Island
, 2004
"... With the development of the semantic web activity, ontologies become widely used to represent the conceptualization of a domain. However, none of the existing ontology languages provides a means to capture uncertainty about the concepts, properties and instances in a domain. Probability theory is a ..."
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Cited by 112 (3 self)
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concept that a given description belongs to, and make more accurate semantic integration possible. One approach to provide such a probabilistic extension to ontology languages is to use Bayesian networks, a widely used graphic model for knowledge representation under uncertainty. In this paper, we present
A Probabilistic Extension of Terminological Logics
, 1994
"... In this report we define a probabilistic extension for a basic terminological knowledge representation languages. Two kinds of probabilistic statements are introduced: statements about conditional probabilities between concepts and statements expressing uncertain knowledge about a specific object. T ..."
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Cited by 1 (0 self)
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In this report we define a probabilistic extension for a basic terminological knowledge representation languages. Two kinds of probabilistic statements are introduced: statements about conditional probabilities between concepts and statements expressing uncertain knowledge about a specific object
A Probabilistic Extension to Ontology Language OWL
"... To support uncertain ontology representation and ontology reasoning and mapping, we propose to incorporate Bayesian networks (BN), a widely used graphic model for knowledge representation under uncertainty and OWL, the de facto industry standard ontology language recommended by W3C. First, OWL is au ..."
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, the BN is completed by constructing conditional probability tables (CPT) for each node in the DAG. Our probabilistic extension to OWL is consistent with OWL semantics, and the translated BN is associated with a joint probability distribution over the application domain. General Bayesian network inference
A probabilistic extension of UML statecharts : specification and verification
 In Werner Damm Probabilistic Extension of UML Statecharts 25 and ErnstRüdiger Olderog, editors, Formal Techniques in RealTime and FaultTolerant Systems : 7th intl. symposium . . . proceedings, volume 2469 of LNCS
, 2002
"... Abstract. This paper is the extended technical report that corresponds to a published paper [14]. This paper introduces means to specify system randomness within UML statecharts, and to verify probabilistic temporal properties over such enhanced statecharts which we call probabilistic UML statechart ..."
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Cited by 18 (3 self)
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Abstract. This paper is the extended technical report that corresponds to a published paper [14]. This paper introduces means to specify system randomness within UML statecharts, and to verify probabilistic temporal properties over such enhanced statecharts which we call probabilistic UML
Bayesian Probabilistic Extensions of a Deterministic Classification Model
, 2000
"... This paper extends deterministic models for Boolean regression within a ..."
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Cited by 3 (1 self)
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This paper extends deterministic models for Boolean regression within a
A Probabilistic Extension of Locally Testable Tree Languages
"... Probabilistic ktestable models (usually known as kgram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events. In this paper we introduce the family of stochastic ktestable tree languages and describe how these models can ..."
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Probabilistic ktestable models (usually known as kgram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events. In this paper we introduce the family of stochastic ktestable tree languages and describe how these models
Probabilistic Extensions of the ErdősKoRado Property
, 2004
"... The classical ErdősKoRado (EKR) Theorem states that if we choose a family of subsets, each of size k, from a fixed set of size n (n> 2k), then the largest possible pairwise intersecting family has size t = (n−1 k−1. We consider the probability that a randomly selected family of size t = tn has ..."
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The classical ErdősKoRado (EKR) Theorem states that if we choose a family of subsets, each of size k, from a fixed set of size n (n> 2k), then the largest possible pairwise intersecting family has size t = (n−1 k−1. We consider the probability that a randomly selected family of size t = tn has the EKR property (pairwise nonempty intersection) as n and k = kn tend to infinity, the latter at a specific rate. As t gets large, the EKR property is less likely to occur, while as t gets smaller, the EKR property is satisfied with high probability. We derive the threshold value for t using Janson’s inequality. Using the SteinChen method we show that the distribution of X0, defined 1 as the number of disjoint pairs of subsets in our family, can be approximated by a Poisson distribution. We extend our results to yield similar conclusions for Xi, the number of pairs of subsets that overlap in exactly i elements. Finally, we show that the joint distribution (X0,X1,...,Xb) can be approximated by a multidimensional Poisson vector with independent components. 1
Results 1  10
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