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On the Accuracy of Stochastic Complexity Approximations
 IN A. GAMMERMAN (ED.), CAUSAL
, 1997
"... Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure is of great theoretical and practical importance as a tool for tasks such as determining model complexity, or performing predictive inference. U ..."
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Cited by 6 (3 self)
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Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure is of great theoretical and practical importance as a tool for tasks such as determining model complexity, or performing predictive inference
Efficient Computation of Stochastic Complexity
 Proceedings of the Ninth International Conference on Artificial Intelligence and Statistics
, 2003
"... Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure is of great theoretical and practical importance as a tool for tasks such as model selection or data clustering. Unfortunately, computing ..."
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Cited by 16 (11 self)
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Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure is of great theoretical and practical importance as a tool for tasks such as model selection or data clustering. Unfortunately, computing
Stochastic Perturbation Theory
, 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
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Cited by 886 (35 self)
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. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variation in the perturbed quantity. Up to the higherorder terms that are ignored in the expansion, these statistics tend to be more realistic than perturbation bounds obtained in terms of norms. The technique is applied to a number of problems in matrix perturbation theory, including least squares and the eigenvalue problem. Key words. perturbation theory, random matrix, linear system, least squares, eigenvalue, eigenvector, invariant subspace, singular value AMS(MOS) subject classifications. 15A06, 15A12, 15A18, 15A52, 15A60 1. Introduction. Let A be a matrix and let F be a matrix valued function of A. Two principal problems of matrix perturbation theory are the following. Given a matrix E, pr...
The Contribution of Parameters to Stochastic Complexity
"... We consider the contribution of parameters to the stochastic complexity. The stochastic complexity of a class of models is the length of a universal, onepart code representing this class. It combines the length of the maximum likelihood code with the parametric complexity, a normalization that acts ..."
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Cited by 4 (0 self)
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We consider the contribution of parameters to the stochastic complexity. The stochastic complexity of a class of models is the length of a universal, onepart code representing this class. It combines the length of the maximum likelihood code with the parametric complexity, a normalization
NEW INSIGHTS ON STOCHASTIC COMPLEXITY
"... The Minimum Description Length (MDL) principle led to various expressions of the stochastic complexity (SC), and the most recent one is given by the negative logarithm of the Normalized Maximum Likelihood (NML). For better understanding the properties of the newest SCformula, we relate it to the we ..."
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Cited by 1 (1 self)
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The Minimum Description Length (MDL) principle led to various expressions of the stochastic complexity (SC), and the most recent one is given by the negative logarithm of the Normalized Maximum Likelihood (NML). For better understanding the properties of the newest SCformula, we relate
The Valuation of Options for Alternative Stochastic Processes
 Journal of Financial Economics
, 1976
"... This paper examines the structure of option valuation problems and develops a new technique for their solution. It also introduces several jump and diffusion processes which have nol been used in previous models. The technique is applied lo these processes to find explicit option valuation formulas, ..."
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Cited by 661 (4 self)
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This paper examines the structure of option valuation problems and develops a new technique for their solution. It also introduces several jump and diffusion processes which have nol been used in previous models. The technique is applied lo these processes to find explicit option valuation formulas, and solutions to some previously unsolved problems involving the pricing ofsecurities with payouts and potential bankruptcy. 1.
Stochastic complexity and Newton diagram
 Proc. of International Symposium on Information Theory and its Applications, ISITA2004
"... Many singular learning machines such as neural networks and mixture models are used in the information engineering field. In spite of their wide range applications, their mathematical foundation of analysis is not yet constructed because of the singularities in the parameter space. In recent years, ..."
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Cited by 1 (1 self)
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method, we obtain the exact value of the asymptotic stochastic complexity, which is a criterion of the model selection, in a mixture of binomial distributions. 1.
Planning and acting in partially observable stochastic domains
 ARTIFICIAL INTELLIGENCE
, 1998
"... In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable mdps (pomdps). We then outline a novel algorithm ..."
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Cited by 1089 (42 self)
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In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable mdps (pomdps). We then outline a novel algorithm
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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simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm  Conditional
Statistical mechanics of complex networks
 Rev. Mod. Phys
"... Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as ra ..."
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Cited by 2083 (10 self)
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Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled
Results 1  10
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