The Experts below are selected from a list of 8493 Experts worldwide ranked by ideXlab platform
Andrea Macrina - One of the best experts on this subject based on the ideXlab platform.
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Conditional Density models for asset pricing
International Journal of Theoretical and Applied Finance, 2012Co-Authors: Damir Filipovic, Lane P Hughston, Andrea MacrinaAbstract:We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the Conditional Probability Density process for the value of an asset at some specified time in the future. In the case where the price process is driven by Brownian motion, an associated "master equation" for the dynamics of the Conditional Probability Density is derived and expressed in integral form. By a "model" for the Conditional Density process we mean a solution to the master equation along with the specification of (a) the initial Density, and (b) the volatility structure of the Density. The volatility structure is assumed at any time and for each value of the argument of the Density to be a functional of the history of the Density up to that time. In practice one specifies the functional modulo sufficient parametric freedom to allow for the input of additional option data apart from that implicit in the initial Density. The scheme is sufficiently flexible to allow for the input of various types of data depending on the nature of the options market and the class of valuation problem being undertaken. Various examples are studied in detail, with exact solutions provided in some cases.
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Conditional Density models for asset pricing
Social Science Research Network, 2010Co-Authors: Damir Filipovic, Lane P Hughston, Andrea MacrinaAbstract:We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the Conditional Probability Density process for the value of an asset at some specified time in the future. In the case where the asset is driven by Brownian motion, an associated "master equation" for the dynamics of the Conditional Probability Density is derived and expressed in integral form. By a "model" for the Conditional Density process we mean a solution to the master equation along with the specification of (a) the initial Density, and (b) the volatility structure of the Density. The volatility structure is assumed at any time and for each value of the argument of the Density to be a functional of the history of the Density up to that time. This functional determines the model for the Conditional Density. In practice one specifies the functional modulo sufficient parametric freedom to allow for the input of additional option data apart from that implicit in the initial Density. The scheme is sufficiently exible to allow for the input of various types of data depending on the nature of the options market and the class of valuation problem being undertaken. Various examples are studied in detail, with exact solutions provided in some cases.
Rachid Deriche - One of the best experts on this subject based on the ideXlab platform.
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geodesic active regions and level set methods for supervised texture segmentation
International Journal of Computer Vision, 2002Co-Authors: Nikos Paragios, Rachid DericheAbstract:This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component Conditional Probability Density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
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geodesic active regions for supervised texture segmentation
International Conference on Computer Vision, 1999Co-Authors: Nikos Paragios, Rachid DericheAbstract:The paper presents a novel variational method for supervised texture segmentation. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component Conditional Probability Density functions. The texture segmentation is obtained by unifying region and boundary based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
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geodesic active contours for supervised texture segmentation
Computer Vision and Pattern Recognition, 1999Co-Authors: N Paraagios, Rachid DericheAbstract:This paper presents a variational method for supervised texture segmentation which is based on ideas coming from the curve propagation theory. We assume that a preferable texture pattern is known (e.g., the pattern that we want to distinguish from the rest of the image). The textured feature space is generated by filtering the input and the preferable pattern image using Gabor filters, and analyzing their responses as multi-component Conditional Probability Density functions. The texture segmentation is obtained by minimizing a Geodesic Active Contour Model objective function where the boundary-based information is expressed via discontinuities on the statistical space associated with the multi-modal textured feature space. This function is minimized using a gradient descent method where the obtained PDE is implemented using a level set approach, that handles naturally the topological changes. Finally a fast method is used for the level set implementation. The performance of our method is demonstrated on a variety of synthetic and real textured images.
Nikos Paragios - One of the best experts on this subject based on the ideXlab platform.
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geodesic active regions and level set methods for supervised texture segmentation
International Journal of Computer Vision, 2002Co-Authors: Nikos Paragios, Rachid DericheAbstract:This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component Conditional Probability Density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
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geodesic active regions for supervised texture segmentation
International Conference on Computer Vision, 1999Co-Authors: Nikos Paragios, Rachid DericheAbstract:The paper presents a novel variational method for supervised texture segmentation. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component Conditional Probability Density functions. The texture segmentation is obtained by unifying region and boundary based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
Sergei K Turitsyn - One of the best experts on this subject based on the ideXlab platform.
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a lower bound on the per soliton capacity of the nonlinear optical fibre channel
Information Theory Workshop, 2015Co-Authors: Nikita A Shevchenko, P Bayvel, Jaroslaw E Prilepsky, Stanislav A Derevyanko, Alex Alvarado, Sergei K TuritsynAbstract:A closed-form expression for a lower bound on the per soliton capacity of the nonlinear optical fibre channel in the presence of (optical) amplifier spontaneous emission (ASE) noise is derived. This bound is based on a non-Gaussian Conditional Probability Density function for the soliton amplitude jitter induced by the ASE noise and is proven to grow logarithmically as the signal-to-noise ratio increases.
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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.
Xiaoyang Li - One of the best experts on this subject based on the ideXlab platform.
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a methodology to determine a Conditional Probability Density distribution surface from s n data
International Journal of Fatigue, 2012Co-Authors: Jianming Zhai, Xiaoyang LiAbstract:A Conditional Probability Density distribution surface (CPDDS) is constructed by two functions in this paper. One is the function of population mean and the stress level; the other is the function of standard deviation and the stress level. The surface represents the Probability Density curve of fatigue life changing with the stress level. A new definition of fatigue limit as well as the determination method is given in this paper according to the small Probability event of CPDDS. The proposed method can save considerable cost and time in the determination of fatigue limit.