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109,437
Modeling conditional probability distributions for periodic variables
 Neural Computation
, 1996
"... ABSTRACT Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply ..."
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Cited by 9 (3 self)
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ABSTRACT Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply
Efficient Bayesian Inference by Factorizing Conditional Probability Distributions
"... Bayesian inference becomes more efficient when one makes use of the structure that is contained within the conditional probability tables that together constitute a joint probability distribution over a set of discrete random variables. Such structure can be represented in the form of probability tr ..."
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Cited by 1 (1 self)
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Bayesian inference becomes more efficient when one makes use of the structure that is contained within the conditional probability tables that together constitute a joint probability distribution over a set of discrete random variables. Such structure can be represented in the form of probability
Kernel Regression by Mode Calculation of the Conditional Probability Distribution
, 811
"... The most direct way to express arbitrary dependencies in datasets is to estimate the joint distribution and to apply afterwards the argmaxfunction to obtain the mode of the corresponding conditional distribution. This method is in practice difficult, because it requires a global optimization of a co ..."
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The most direct way to express arbitrary dependencies in datasets is to estimate the joint distribution and to apply afterwards the argmaxfunction to obtain the mode of the corresponding conditional distribution. This method is in practice difficult, because it requires a global optimization of a
Games with Incomplete Information Played by 'Bayesian' Players, IIII
 MANAGEMENT SCIENCE
, 1967
"... The paper develops a new theory for the analysis of games with incomplete information where the players are uncertain about some important parameters of the game situation, such as the payoff functions, the strategies available to various players, the information other players have about the game, e ..."
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Cited by 787 (2 self)
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, etc However, each player has a subjective probability distribution over the alternative possibibties In most of the paper it is assumed that these probability distributions entertained by the different players are mutually "consistent", in the sense that they can be regarded as conditional
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 881 (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
Contour Tracking By Stochastic Propagation of Conditional Density
, 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
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Cited by 661 (23 self)
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with learned dynamical models to propagate an entire probability distribution for object pos...
CONDENSATION  conditional density propagation for visual tracking
, 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to th ..."
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Cited by 1503 (12 self)
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to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion
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 ..."
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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
Markov chains for exploring posterior distributions
 Annals of Statistics
, 1994
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 1136 (6 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Estimating the Support of a HighDimensional 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 ..."
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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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109,437