The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Sergei K Turitsyn - One of the best experts on this subject based on the ideXlab platform.
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Conditional Probability calculations for the nonlinear schrodinger equation with additive noise
Physical Review Letters, 2014Co-Authors: I S Terekhov, S S Vergeles, Sergei K TuritsynAbstract:The method for the computation of the Conditional Probability density function for the nonlinear Schrodinger equation with additive noise is developed. We present in a constructive form the Conditional Probability density function in the limit of small noise and analytically derive it in a weakly nonlinear case. The general theory results are illustrated using fiber-optic communications as a particular, albeit practically very important, example.
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Conditional Probability calculations for the nonlinear schr odinger equation with additive noise
arXiv: Information Theory, 2014Co-Authors: I S Terekhov, S S Vergeles, Sergei K TuritsynAbstract:The method for computation of Conditional Probability density function for the nonlinear Schr\"odinger equation with additive noise is developed. We present in a constructive form the Conditional Probability density function in the limit of a small noise and analytically derive it in a weakly nonlinear case. The general theory results are illustrated using fibre-optic communications as a particular, albeit practically very important, example.
I.t. Nabney - One of the best experts on this subject based on the ideXlab platform.
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Modeling Conditional Probability Distributions for Periodic Variables
Neural Computation, 1996Co-Authors: C.m. Bishop, I.t. NabneyAbstract:Most conventional techniques for estimating Conditional Probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.
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Modelling Conditional Probability distributions for periodic variables
1995 Fourth International Conference on Artificial Neural Networks, 1995Co-Authors: I.t. Nabney, C.m. Bishop, C. LegleyeAbstract: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 techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.
V. Vapnik - One of the best experts on this subject based on the ideXlab platform.
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SVM method of estimating density, Conditional Probability, and Conditional density
2000 IEEE International Symposium on Circuits and Systems (ISCAS), 2000Co-Authors: V. VapnikAbstract:The problem of estimating density, Conditional Probability, and Conditional density is considered as an ill-posed problem of solving integral equations. To solve these equations the support vector method (SVM) is used.
I S Terekhov - One of the best experts on this subject based on the ideXlab platform.
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Conditional Probability calculations for the nonlinear schrodinger equation with additive noise
Physical Review Letters, 2014Co-Authors: I S Terekhov, S S Vergeles, Sergei K TuritsynAbstract:The method for the computation of the Conditional Probability density function for the nonlinear Schrodinger equation with additive noise is developed. We present in a constructive form the Conditional Probability density function in the limit of small noise and analytically derive it in a weakly nonlinear case. The general theory results are illustrated using fiber-optic communications as a particular, albeit practically very important, example.
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Conditional Probability calculations for the nonlinear schr odinger equation with additive noise
arXiv: Information Theory, 2014Co-Authors: I S Terekhov, S S Vergeles, Sergei K TuritsynAbstract:The method for computation of Conditional Probability density function for the nonlinear Schr\"odinger equation with additive noise is developed. We present in a constructive form the Conditional Probability density function in the limit of a small noise and analytically derive it in a weakly nonlinear case. The general theory results are illustrated using fibre-optic communications as a particular, albeit practically very important, example.
Joseph Y Halpern - One of the best experts on this subject based on the ideXlab platform.
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lexicographic Probability Conditional Probability and nonstandard Probability
arXiv: Computer Science and Game Theory, 2003Co-Authors: Joseph Y HalpernAbstract:The relationship between Popper spaces (Conditional Probability spaces that satisfy some regularity conditions), lexicographic Probability systems (LPS's), and nonstandard Probability spaces (NPS's) is considered. If countable additivity is assumed, Popper spaces and a subclass of LPS's are equivalent; without the assumption of countable additivity, the equivalence no longer holds. If the state space is finite, LPS's are equivalent to NPS's. However, if the state space is infinite, NPS's are shown to be more general than LPS's.
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lexicographic Probability Conditional Probability and nonstandard Probability
Theoretical Aspects of Rationality and Knowledge, 2001Co-Authors: Joseph Y HalpernAbstract:The relationship between Popper spaces (Conditional Probability spaces that satisfy some regularity conditions), lexicographic Probability systems (LPS's) [Blume, Brandenburger, and Dekel 1991a; Blume, Brandenburger, and Dekel 1991b], and nonstandard Probability spaces (NPS's) is considered. If countable additivity is assumed, Popper spaces and a subclass of LPS's are equivalent; without the assumption of countable additivity, the equivalence no longer holds. If the state space is finite, LPS's are equivalent to NPS's. However, if the state space is infinite, NPS's are shown to be more general than LPS's.