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Results 1 - 10
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54,653
Some properties of joint probability distributions, in
- Proceedings of the 10th Conference on Uncertainty in Artificial 6 Intelligence, UAI-94
, 1994
"... Abstract Several Artifi cial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncer tain domain models. Even though the correct working of these schemes is practically con tingent upon the existen ..."
Abstract
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Cited by 29 (7 self)
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the existence 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 probabili ties of various states of uncertain models. By rewriting the joint probability distribu tion 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. ..."
Abstract
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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 ..."
Abstract
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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 spec-ified. 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 spec-ified. 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 gen-erating 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 gen-erating 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 n-dimensional discrete probability distribution by a product of second-order distributions, or the distribution of the first-order tree dependence. The problem is to find an optimum set of n-1 first order dependence relationship among the n variables ..."
Abstract
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Cited by 881 (0 self)
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A method is presented to approximate optimally an n-dimensional discrete probability distribution by a product of second-order distributions, or the distribution of the first-order tree dependence. The problem is to find an optimum set of n-1 first order dependence relationship among the n
Distributional Clustering Of English Words
- In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics
, 1993
"... We describe and evaluate experimentally a method for clustering words according to their dis- tribution in particular syntactic contexts. Words are represented by the relative frequency distributions of contexts in which they appear, and relative entropy between those distributions is used as the si ..."
Abstract
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Cited by 629 (27 self)
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as the similarity measure for clustering. Clusters are represented by average context distributions derived from the given words according to their probabilities of cluster membership. In many cases, the clusters can be thought of as encoding coarse sense distinctions. Deterministic annealing is used to find lowest
Estimating the Support of a High-Dimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
Abstract
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Cited by 783 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Results 1 - 10
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54,653
