The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
J.d. Scargle - One of the best experts on this subject based on the ideXlab platform.
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Joint Segmentation of Multivariate Astronomical Time Series: Bayesian Sampling With a Hierarchical Model
IEEE Transactions on Signal Processing, 2007Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:Astronomy and other sciences often face the problem of detecting and characterizing structure in two or more related time series. This paper approaches such problems using Bayesian priors to represent relationships between signals with various degrees of certainty, and not just rigid constraints. The segmentation is conducted by using a hierarchical Bayesian approach to a piecewise Constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results obtained with synthetic and real photon counting data illustrate the performance of the proposed algorithm
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EUSIPCO - Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments
2006Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:This paper addresses the problem of detecting significant intensity variations in multiple Poissonian time-series. This detection is achieved by using a Constant Poisson rate model and a hierarchical Bayesian approach. An appropriate Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. An extended model that includes constraints on the segment lengths is also proposed. Simulation results performed on synthetic and real data illustrate the performance of the proposed algorithm.
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Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments
2006 14th European Signal Processing Conference, 2006Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:This paper addresses the problem of detecting significant intensity variations in multiple Poissonian time-series. This detection is achieved by using a Constant Poisson rate model and a hierarchical Bayesian approach. An appropriate Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. An extended model that includes constraints on the segment lengths is also proposed. Simulation results performed on synthetic and real data illustrate the performance of the proposed algorithm.
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Change-point detection in astronomical data by using a hierarchical model and a bayesian sampling approach
IEEE SP 13th Workshop on Statistical Signal Processing 2005, 2005Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:Detection of significant intensity variations in astronomical time-series can be achieved with a hierarchical Bayesian approach to a piecewise Constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results with real and synthetic photon counting data illustrate the performance of the proposed algorithm. An extension to joint segmentation of multiple time series is also discussed
N. Dobigeon - One of the best experts on this subject based on the ideXlab platform.
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Joint Segmentation of Multivariate Astronomical Time Series: Bayesian Sampling With a Hierarchical Model
IEEE Transactions on Signal Processing, 2007Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:Astronomy and other sciences often face the problem of detecting and characterizing structure in two or more related time series. This paper approaches such problems using Bayesian priors to represent relationships between signals with various degrees of certainty, and not just rigid constraints. The segmentation is conducted by using a hierarchical Bayesian approach to a piecewise Constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results obtained with synthetic and real photon counting data illustrate the performance of the proposed algorithm
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EUSIPCO - Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments
2006Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:This paper addresses the problem of detecting significant intensity variations in multiple Poissonian time-series. This detection is achieved by using a Constant Poisson rate model and a hierarchical Bayesian approach. An appropriate Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. An extended model that includes constraints on the segment lengths is also proposed. Simulation results performed on synthetic and real data illustrate the performance of the proposed algorithm.
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Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments
2006 14th European Signal Processing Conference, 2006Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:This paper addresses the problem of detecting significant intensity variations in multiple Poissonian time-series. This detection is achieved by using a Constant Poisson rate model and a hierarchical Bayesian approach. An appropriate Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. An extended model that includes constraints on the segment lengths is also proposed. Simulation results performed on synthetic and real data illustrate the performance of the proposed algorithm.
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Change-point detection in astronomical data by using a hierarchical model and a bayesian sampling approach
IEEE SP 13th Workshop on Statistical Signal Processing 2005, 2005Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:Detection of significant intensity variations in astronomical time-series can be achieved with a hierarchical Bayesian approach to a piecewise Constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results with real and synthetic photon counting data illustrate the performance of the proposed algorithm. An extension to joint segmentation of multiple time series is also discussed
J. Tourneret - One of the best experts on this subject based on the ideXlab platform.
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Joint Segmentation of Multivariate Astronomical Time Series: Bayesian Sampling With a Hierarchical Model
IEEE Transactions on Signal Processing, 2007Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:Astronomy and other sciences often face the problem of detecting and characterizing structure in two or more related time series. This paper approaches such problems using Bayesian priors to represent relationships between signals with various degrees of certainty, and not just rigid constraints. The segmentation is conducted by using a hierarchical Bayesian approach to a piecewise Constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results obtained with synthetic and real photon counting data illustrate the performance of the proposed algorithm
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EUSIPCO - Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments
2006Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:This paper addresses the problem of detecting significant intensity variations in multiple Poissonian time-series. This detection is achieved by using a Constant Poisson rate model and a hierarchical Bayesian approach. An appropriate Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. An extended model that includes constraints on the segment lengths is also proposed. Simulation results performed on synthetic and real data illustrate the performance of the proposed algorithm.
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Joint segmentation of multivariate Poissonian time series. Application to burst and transient source experiments
2006 14th European Signal Processing Conference, 2006Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:This paper addresses the problem of detecting significant intensity variations in multiple Poissonian time-series. This detection is achieved by using a Constant Poisson rate model and a hierarchical Bayesian approach. An appropriate Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. An extended model that includes constraints on the segment lengths is also proposed. Simulation results performed on synthetic and real data illustrate the performance of the proposed algorithm.
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Change-point detection in astronomical data by using a hierarchical model and a bayesian sampling approach
IEEE SP 13th Workshop on Statistical Signal Processing 2005, 2005Co-Authors: N. Dobigeon, J. Tourneret, J.d. ScargleAbstract:Detection of significant intensity variations in astronomical time-series can be achieved with a hierarchical Bayesian approach to a piecewise Constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results with real and synthetic photon counting data illustrate the performance of the proposed algorithm. An extension to joint segmentation of multiple time series is also discussed
Bruce Schmeiser - One of the best experts on this subject based on the ideXlab platform.
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Winter Simulation Conference - I-SMOOTH: iteratively smoothing piecewise-Constant Poisson-process rate functions
Proceedings of the 2011 Winter Simulation Conference (WSC), 2011Co-Authors: Huifen Chen, Bruce SchmeiserAbstract:Piecewise-Constant Poisson process rate functions are easy to estimate and provide easy random-process generation. When the true rate function is continuous, however, a piecewise-Constant approximation is sometimes unacceptably crude. Given a non-negative piecewise-Constant rate function, we discuss SMOOTH (Smoothing via Mean-constrained Optimized-Objective Time Halving), a quadratic optimization formulation that yields a smoother non-negative piecewise-Constant rate function having twice as many time intervals, each of half the length. I-SMOOTH (Iterated SMOOTH) iterates the SMOOTH formulation to create a sequence of piecewise-Constant rate functions having an asymptotic continuous rate function. We consider two contexts: finite-horizon and cyclic. We develop a sequence of computational simplifications for SMOOTH, moving from numerically minimizing the quadratic objective function, to numerically computing a matrix inverse, to a closed-form matrix inverse obtained as finite sums, to decision variables that are linear combinations of the given rates, and to simple approximations.
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I-SMOOTH: Iteratively smoothing piecewise-Constant Poisson-process rate functions
Proceedings of the 2011 Winter Simulation Conference (WSC), 2011Co-Authors: Huifen Chen, Bruce SchmeiserAbstract:Piecewise-Constant Poisson process rate functions are easy to estimate and provide easy random-process generation. When the true rate function is continuous, however, a piecewise-Constant approximation is sometimes unacceptably crude. Given a non-negative piecewise-Constant rate function, we discuss SMOOTH (Smoothing via Mean-constrained Optimized-Objective Time Halving), a quadratic optimization formulation that yields a smoother non-negative piecewise-Constant rate function having twice as many time intervals, each of half the length. I-SMOOTH (Iterated SMOOTH) iterates the SMOOTH formulation to create a sequence of piecewise-Constant rate functions having an asymptotic continuous rate function. We consider two contexts: finite-horizon and cyclic. We develop a sequence of computational simplifications for SMOOTH, moving from numericallyminimizing the quadratic objective function, to numerically computing a matrix inverse, to a closed-form matrix inverse obtained as finite sums, to decision variables that are linear combinations of the given rates, and to simple approximations.
Huifen Chen - One of the best experts on this subject based on the ideXlab platform.
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Winter Simulation Conference - I-SMOOTH: iteratively smoothing piecewise-Constant Poisson-process rate functions
Proceedings of the 2011 Winter Simulation Conference (WSC), 2011Co-Authors: Huifen Chen, Bruce SchmeiserAbstract:Piecewise-Constant Poisson process rate functions are easy to estimate and provide easy random-process generation. When the true rate function is continuous, however, a piecewise-Constant approximation is sometimes unacceptably crude. Given a non-negative piecewise-Constant rate function, we discuss SMOOTH (Smoothing via Mean-constrained Optimized-Objective Time Halving), a quadratic optimization formulation that yields a smoother non-negative piecewise-Constant rate function having twice as many time intervals, each of half the length. I-SMOOTH (Iterated SMOOTH) iterates the SMOOTH formulation to create a sequence of piecewise-Constant rate functions having an asymptotic continuous rate function. We consider two contexts: finite-horizon and cyclic. We develop a sequence of computational simplifications for SMOOTH, moving from numerically minimizing the quadratic objective function, to numerically computing a matrix inverse, to a closed-form matrix inverse obtained as finite sums, to decision variables that are linear combinations of the given rates, and to simple approximations.
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I-SMOOTH: Iteratively smoothing piecewise-Constant Poisson-process rate functions
Proceedings of the 2011 Winter Simulation Conference (WSC), 2011Co-Authors: Huifen Chen, Bruce SchmeiserAbstract:Piecewise-Constant Poisson process rate functions are easy to estimate and provide easy random-process generation. When the true rate function is continuous, however, a piecewise-Constant approximation is sometimes unacceptably crude. Given a non-negative piecewise-Constant rate function, we discuss SMOOTH (Smoothing via Mean-constrained Optimized-Objective Time Halving), a quadratic optimization formulation that yields a smoother non-negative piecewise-Constant rate function having twice as many time intervals, each of half the length. I-SMOOTH (Iterated SMOOTH) iterates the SMOOTH formulation to create a sequence of piecewise-Constant rate functions having an asymptotic continuous rate function. We consider two contexts: finite-horizon and cyclic. We develop a sequence of computational simplifications for SMOOTH, moving from numericallyminimizing the quadratic objective function, to numerically computing a matrix inverse, to a closed-form matrix inverse obtained as finite sums, to decision variables that are linear combinations of the given rates, and to simple approximations.