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Modelling Conditional Probability Distributions for Periodic Variables
 Neural Computation
, 1996
"... 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 these te ..."
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Cited by 9 (3 self)
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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 765 (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 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.
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 658 (24 self)
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with learned dynamical models to propagate an entire probability distribution for object pos...
Shallow Parsing with Conditional Random Fields
, 2003
"... Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluati ..."
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Cited by 575 (8 self)
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Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard
CONDENSATION  conditional density propagation for visual tracking
 International Journal of Computer Vision
, 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 appli ..."
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Cited by 1499 (12 self)
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applied 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
Results 1  10
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