The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Chintha Tellambura - One of the best experts on this subject based on the ideXlab platform.
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Infinite Series Representations of the Trivariate and Quadrivariate Nakagami-m distributions
2007 IEEE International Conference on Communications, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive new infinite series representations for the quadrivariate Nakagami-m distribution and cumulative distribution Functions (cdf). we make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. The classical Joint Density Function of exponentially correlated Nakagami-m variables can be identified as a special case of our Joint Density Function. Our results are based on the most general arbitrary correlation matrix possible. Moreover, the trivariate Density Function and cdf for an arbitrary correlation matrix is also derived from our main result. Bounds on the error resulting from truncation of the infinite series are also presented. Finally, numerical results are presented to verify the accuracy of our formulation.
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ICC - Infinite Series Representations of the Trivariate and Quadrivariate Nakagami-m distributions
2007 IEEE International Conference on Communications, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive new infinite series representations for the quadrivariate Nakagami-m distribution and cumulative distribution Functions (cdf). we make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. The classical Joint Density Function of exponentially correlated Nakagami-m variables can be identified as a special case of our Joint Density Function. Our results are based on the most general arbitrary correlation matrix possible. Moreover, the trivariate Density Function and cdf for an arbitrary correlation matrix is also derived from our main result. Bounds on the error resulting from truncation of the infinite series are also presented. Finally, numerical results are presented to verify the accuracy of our formulation.
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On the Trivariate Non-Central Chi-Squared Distribution
2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive a new infinite series representation for the trivariate non-central chi-squared distribution when the underlying correlated Gaussian variables have tridiagonal form of inverse covariance matrix. We make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. Moreover, the trivariate cumulative distribution Function (cdf) and characteristic Function (chf) are also derived. Finally, bivariate noncentral chi-squared distribution and some known forms are shown to be special cases of the more general distribution. However, non-central chi-squared distribution for an arbitrary covariance matrix seems intractable with the Miller's approach.
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VTC Spring - On the Trivariate Non-Central Chi-Squared Distribution
2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive a new infinite series representation for the trivariate non-central chi-squared distribution when the underlying correlated Gaussian variables have tridiagonal form of inverse covariance matrix. We make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. Moreover, the trivariate cumulative distribution Function (cdf) and characteristic Function (chf) are also derived. Finally, bivariate noncentral chi-squared distribution and some known forms are shown to be special cases of the more general distribution. However, non-central chi-squared distribution for an arbitrary covariance matrix seems intractable with the Miller's approach.
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Infinite series representations of the trivariate and quadrivariate nakagami-m distributions
IEEE Transactions on Wireless Communications, 2007Co-Authors: Prathapasinghe Dharmawansa, Nandana Rajatheva, Chintha TellamburaAbstract:In this paper, using Miller's approach and Dougall's identity, we derive new infinite series representations for the quadrivariate Nakagami-m Joint Density Function, cumulative distribution Function (cdf) and characteristic Functions (chf). The classical Joint Density Function of exponentially correlated Nakagami-m variables can be identified as a special case of the Joint Density Function obtained here. Our results are based on the most general arbitrary correlation matrix possible. Moreover, the trivariate Density Function, cdf and chf for an arbitrary correlation matrix are also derived from our main result. Bounds on the series truncation error are also presented. Finally, we develop several representative applications: the outage probability of triple branch selection combining (SC), the moments of the equal gain combining (EGC) output signal to noise ratio (SNR) and the moment generation Function of the generalized SC(2,3) output SNR in an arbitrarily correlated Nakagami-m environment. Simulation results are also presented to verify the accuracy of our theoretical results.
K. D. P. Dharmawansa - One of the best experts on this subject based on the ideXlab platform.
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Infinite Series Representations of the Trivariate and Quadrivariate Nakagami-m distributions
2007 IEEE International Conference on Communications, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive new infinite series representations for the quadrivariate Nakagami-m distribution and cumulative distribution Functions (cdf). we make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. The classical Joint Density Function of exponentially correlated Nakagami-m variables can be identified as a special case of our Joint Density Function. Our results are based on the most general arbitrary correlation matrix possible. Moreover, the trivariate Density Function and cdf for an arbitrary correlation matrix is also derived from our main result. Bounds on the error resulting from truncation of the infinite series are also presented. Finally, numerical results are presented to verify the accuracy of our formulation.
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ICC - Infinite Series Representations of the Trivariate and Quadrivariate Nakagami-m distributions
2007 IEEE International Conference on Communications, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive new infinite series representations for the quadrivariate Nakagami-m distribution and cumulative distribution Functions (cdf). we make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. The classical Joint Density Function of exponentially correlated Nakagami-m variables can be identified as a special case of our Joint Density Function. Our results are based on the most general arbitrary correlation matrix possible. Moreover, the trivariate Density Function and cdf for an arbitrary correlation matrix is also derived from our main result. Bounds on the error resulting from truncation of the infinite series are also presented. Finally, numerical results are presented to verify the accuracy of our formulation.
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On the Trivariate Non-Central Chi-Squared Distribution
2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive a new infinite series representation for the trivariate non-central chi-squared distribution when the underlying correlated Gaussian variables have tridiagonal form of inverse covariance matrix. We make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. Moreover, the trivariate cumulative distribution Function (cdf) and characteristic Function (chf) are also derived. Finally, bivariate noncentral chi-squared distribution and some known forms are shown to be special cases of the more general distribution. However, non-central chi-squared distribution for an arbitrary covariance matrix seems intractable with the Miller's approach.
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VTC Spring - On the Trivariate Non-Central Chi-Squared Distribution
2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007Co-Authors: K. D. P. Dharmawansa, R. M. A. P. Rajatheva, Chintha TellamburaAbstract:In this paper, we derive a new infinite series representation for the trivariate non-central chi-squared distribution when the underlying correlated Gaussian variables have tridiagonal form of inverse covariance matrix. We make use of the Miller's approach and the Dougall's identity to derive the Joint Density Function. Moreover, the trivariate cumulative distribution Function (cdf) and characteristic Function (chf) are also derived. Finally, bivariate noncentral chi-squared distribution and some known forms are shown to be special cases of the more general distribution. However, non-central chi-squared distribution for an arbitrary covariance matrix seems intractable with the Miller's approach.
Nasser Kehtarnavaz - One of the best experts on this subject based on the ideXlab platform.
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A new stochastic image model based on Markov random fields and its application to texture modeling
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Siamak Yousefi, Nasser KehtarnavazAbstract:Stochastic image modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic image model based on Markov random fields is introduced in this paper which overcomes the shortcomings of the conventional models easing the computation of the Joint Density Function of images. As an application, this model is used to generate texture patterns. The lower computational complexity and easily controllable parameters of the model makes it a more useful model as compared to the conventional Markov random field-based models.
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ICASSP - A new stochastic image model based on Markov random fields and its application to texture modeling
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Siamak Yousefi, Nasser KehtarnavazAbstract:Stochastic image modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic image model based on Markov random fields is introduced in this paper which overcomes the shortcomings of the conventional models easing the computation of the Joint Density Function of images. As an application, this model is used to generate texture patterns. The lower computational complexity and easily controllable parameters of the model makes it a more useful model as compared to the conventional Markov random field-based models.
V. S. Rao Gudimetla - One of the best experts on this subject based on the ideXlab platform.
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Four-point Joint Density Function for the intensities of a fully developed monochromatic laser speckle pattern
Journal of The Optical Society of America A-optics Image Science and Vision, 1992Co-Authors: V. S. Rao GudimetlaAbstract:A four-point Joint Density Function for the intensities of a fully developed speckle pattern is derived as an infinite summation of products of modified Bessel Functions of the first kind. It is found that the special cases of this Density Function will agree with all the previously known results. With interest growing rapidly in the higher-order correlations of signals, this result will be useful in general optical scattering problems.
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Three-point Joint Density Functions for the intensities of partially and fully developed monochromatic laser speckle patterns
Journal of The Optical Society of America A-optics Image Science and Vision, 1991Co-Authors: V. S. Rao GudimetlaAbstract:A three-point Joint Density Function for the intensity of a partially developed laser speckle pattern is derived as an infinite summation of products of modified Bessel Functions of the first kind. From this summation, a simpler expression for the corresponding Density Function of a fully developed Gaussian speckle is derived. The results will be useful in obtaining the conditional Density Functions of intensity for generai optical scattering problems and for estimating the probability of error in the signal-to-noise ratio calculations in optical Systems such as optical radar.
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Two-point Joint-Density Function of the intensity for a laser-generated speckle field after propagation through the turbulent atmosphere
Journal of The Optical Society of America A-optics Image Science and Vision, 1990Co-Authors: V. S. Rao Gudimetla, J. Fred Holmes, R. A. ElliottAbstract:A two-point, Joint-probability-Density Function for the intensity of a laser-generated speckle field after propagation through turbulence is developed by using the method of compound distributions. The parameters of the Joint-Density Function are derived in terms of the path length, strength of turbulence, wave number, beam size, and degree of focus. Closed-form expressions for some of the Joint moments of the intensity are developed and compared with experimental data. Good agreement is obtained.
Prakash P. Shenoy - One of the best experts on this subject based on the ideXlab platform.
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Operations for inference in continuous Bayesian networks with linear deterministic variables
International Journal of Approximate Reasoning, 2020Co-Authors: Barry R. Cobb, Prakash P. ShenoyAbstract:AbstractAn important class of continuous Bayesian networks are those that have linear conditionally deterministic variables (a variable that is a linear deterministic Function of its parents). In this case, the Joint Density Function for the variables in the network does not exist. Conditional linear Gaussian (CLG) distributions can handle such cases when all variables are normally distributed. In this paper, we develop operations required for performing inference with linear conditionally deterministic variables in continuous Bayesian networks using relationships derived from Joint cumulative distribution Functions. These methods allow inference in networks with linear deterministic variables and non-Gaussian distributions
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Hybrid Bayesian Networks with Linear Deterministic Variables
arXiv: Artificial Intelligence, 2012Co-Authors: Barry R. Cobb, Prakash P. ShenoyAbstract:When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the Joint Density Function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations required for performing inference with conditionally deterministic variables in hybrid Bayesian networks are developed. These methods allow inference in networks with deterministic variables where continuous variables may be non-Gaussian, and their Density Functions can be approximated by mixtures of truncated exponentials. There are no constraints on the placement of continuous and discrete nodes in the network.
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UAI - Hybrid Bayesian networks with linear deterministic variables
2005Co-Authors: Barry R. Cobb, Prakash P. ShenoyAbstract:When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the Joint Density Function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations required for performing inference with conditionally deterministic variables in hybrid Bayesian networks are developed. These methods allow inference in networks with deterministic variables where continuous variables may be non-Gaussian, and their Density Functions can be approximated by mixtures of truncated exponentials. There are no constraints on the placement of continuous and discrete nodes in the network.
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ECSQARU - Nonlinear deterministic relationships in bayesian networks
Lecture Notes in Computer Science, 2005Co-Authors: Barry R. Cobb, Prakash P. ShenoyAbstract:In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic Function of its continuous parents, the Joint Density Function does not exist. Conditional linear Gaussian distributions can handle such cases when the deterministic Function is linear and the continuous variables have a multi-variate normal distribution. In this paper, operations required for performing inference with nonlinear conditionally deterministic variables are developed. We perform inference in networks with nonlinear deterministic variables and non-Gaussian continuous variables by using piecewise linear approximations to nonlinear Functions and modeling probability distributions with mixtures of truncated exponentials (MTE) potentials.
