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Some Properties of Joint Probability Distributions
 In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence (UAI–94
, 1994
"... Several Artificial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncertain domain models. Even though the correct working of these schemes is practically contingent upon the existence of a ..."
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Cited by 30 (7 self)
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of a small number of probable states, no formal justification has been proposed of why this should be the case. This paper attempts to fill this apparent gap by studying asymmetries among probabilities of various states of uncertain models. By rewriting the joint probability distribution over a
On Multifractal Property of the Joint Probability Distributions and Its Application to Bayesian Network Inference
"... This paper demonstrates that the Joint Probability Distribution (JPD) of a Bayesian network is a random multinomial multifractal. ..."
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Cited by 2 (1 self)
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This paper demonstrates that the Joint Probability Distribution (JPD) of a Bayesian network is a random multinomial multifractal.
Full counting statistics for noninteracting fermions: Joint probability distributions
, 904
"... Abstract. The joint probability distribution in the full counting statistics (FCS) for noninteracting electrons is discussed for an arbitrary number of initially separate subsystems which are connected at t = 0 and separated again at a later time. A simple method to obtain the leading order long tim ..."
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Cited by 1 (0 self)
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Abstract. The joint probability distribution in the full counting statistics (FCS) for noninteracting electrons is discussed for an arbitrary number of initially separate subsystems which are connected at t = 0 and separated again at a later time. A simple method to obtain the leading order long
Approximating Joint Probability Distributions Given Partial Information
"... In this paper, we propose new methods to approximate probability distributions that are incompletely specified. We compare these methods to the use of maximum entropy and quantify the accuracy of all methods within the context of an illustrative example. We show that within the context of our examp ..."
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In this paper, we propose new methods to approximate probability distributions that are incompletely specified. We compare these methods to the use of maximum entropy and quantify the accuracy of all methods within the context of an illustrative example. We show that within the context of our
Extreme Points of the Convex Set of Joint Probability Distributions with Fixed Marginals
, 2007
"... Summary: By using a quantum probabilistic approach we obtain a description of the extreme points of the convex set of all joint probability distributions on the product of two standard Borel spaces with fixed marginal distributions. Key words: C ∗ algebra, covariant bistochastic maps, completely pos ..."
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Cited by 1 (0 self)
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Summary: By using a quantum probabilistic approach we obtain a description of the extreme points of the convex set of all joint probability distributions on the product of two standard Borel spaces with fixed marginal distributions. Key words: C ∗ algebra, covariant bistochastic maps, completely
Generating a Random Collection of Discrete Joint Probability Distributions Subject to Partial Information
, 2011
"... Abstract In this paper, we develop a practical and flexible methodology for generating a random collection of discrete joint probability distributions, subject to a specified information set, which can be expressed as a set of linear constraints (e.g., marginal assessments, moments, or pairwise cor ..."
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Abstract In this paper, we develop a practical and flexible methodology for generating a random collection of discrete joint probability distributions, subject to a specified information set, which can be expressed as a set of linear constraints (e.g., marginal assessments, moments, or pairwise
Approximating discrete probability distributions with dependence trees
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1968
"... A method is presented to approximate optimally an ndimensional discrete probability distribution by a product of secondorder distributions, or the distribution of the firstorder tree dependence. The problem is to find an optimum set of n1 first order dependence relationship among the n variables ..."
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Cited by 874 (0 self)
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A method is presented to approximate optimally an ndimensional discrete probability distribution by a product of secondorder distributions, or the distribution of the firstorder tree dependence. The problem is to find an optimum set of n1 first order dependence relationship among the n
DISTRIBUTED SYSTEMS
, 1985
"... Growth of distributed systems has attained unstoppable momentum. If we better understood how to think about, analyze, and design distributed systems, we could direct their implementation with more confidence. ..."
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Cited by 755 (1 self)
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Growth of distributed systems has attained unstoppable momentum. If we better understood how to think about, analyze, and design distributed systems, we could direct their implementation with more confidence.
Estimating Continuous Distributions in Bayesian Classifiers
 In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence
, 1995
"... When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality ..."
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Cited by 489 (2 self)
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When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon
Results 1  10
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2,862,500