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Probabilistic Conditional Preference Networks Damien Bigot
, 2014
"... This paper proposes a “probabilistic ” extension of conditional preference networks as a way to compactly represent a probability distributions over preference orderings. It studies the probabilistic counterparts of the main reasoning tasks, namely dominance testing and optimisation from the algorit ..."
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This paper proposes a “probabilistic ” extension of conditional preference networks as a way to compactly represent a probability distributions over preference orderings. It studies the probabilistic counterparts of the main reasoning tasks, namely dominance testing and optimisation from
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
Discriminative probabilistic models for relational data
, 2002
"... In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, igno ..."
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Cited by 415 (12 self)
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, ignoring the correlations between them. Recently, Probabilistic Relational Models, a relational version of Bayesian networks, were used to define a joint probabilistic model for a collection of related entities. In this paper, we present an alternative framework that builds on (conditional) Markov networks
CPnets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2004
"... Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dep ..."
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Cited by 317 (4 self)
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Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional
Approximating probabilistic inference in Bayesian belief networks is NPhard
, 1991
"... Abstract A belief network comprises a graphical representation of dependencies between variables of a domain and a set of conditional probabilities associated with each dependency. Unless P=NP, an efficient, exact algorithm does not exist to compute probabilistic inference in belief networks. Stoch ..."
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Cited by 291 (4 self)
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Abstract A belief network comprises a graphical representation of dependencies between variables of a domain and a set of conditional probabilities associated with each dependency. Unless P=NP, an efficient, exact algorithm does not exist to compute probabilistic inference in belief networks
Voting with CPnets using a probabilistic preference structure
 IN PROC. COMSOC
, 2014
"... Probabilistic conditional preference networks (PCPnets) provide a compact representation of a probability distribution over a collection of CPnets. In this paper we view a PCPnet as the result of aggregating a collection of CPnets into a single structure. We use the resulting PCPnet to perform ..."
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Cited by 3 (3 self)
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Probabilistic conditional preference networks (PCPnets) provide a compact representation of a probability distribution over a collection of CPnets. In this paper we view a PCPnet as the result of aggregating a collection of CPnets into a single structure. We use the resulting PCPnet to perform
Distributed Localization in Wireless Sensor Networks: A Quantitative Comparison
, 2003
"... This paper studies the problem of determining the node locations in adhoc sensor networks. We compare three distributed localization algorithms (Adhoc positioning, Robust positioning, and Nhop multilateration) on a single simulation platform. The algorithms share a common, threephase structure: ..."
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Cited by 302 (7 self)
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conclusion is that no single algorithm performs best; which algorithm is to be preferred depends on the conditions (range errors, connectivity, anchor fraction, etc.). In each case, however, there is significant room for improving accuracy and/or increasing coverage.
Adaptive Probabilistic Networks with Hidden Variables
 Machine Learning
, 1997
"... . Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are rapidly becoming the tool of choice for uncertain reasoning in artificial intelligence. In this paper, we investigate the problem ..."
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Cited by 176 (9 self)
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. Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are rapidly becoming the tool of choice for uncertain reasoning in artificial intelligence. In this paper, we investigate
Graphical models for preference and utility
 In Proc. UAI
, 1995
"... Probabilistic independence can dramatically simplify the task of eliciting, representing, and computing with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing notions of independence for utility functions in a multiattr ..."
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Cited by 153 (1 self)
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attribute space, and suggest that these can be used to achieve similar advantages. Our new results concern conditional additive independence, which we show always has a perfect representation as separation in an undirected graph (a Markov network). Conditional additive independencies entail a particular
Dependency networks for inference, collaborative filtering, and data visualization
 Journal of Machine Learning Research
"... We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set of ..."
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Cited by 208 (12 self)
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We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set
Results 1  10
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1,911