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45,772
The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Statistical Decision Theory
, 2002
"... This note shows that when the Bayes formula and certain game theory concepts are appropriately applied, the posterior probability of winning a prize in the famous Monty Hall game is not the counterintuitive answer of 2/3 but it is . The moral of this exercise is that the Bayes formula should not be ..."
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This note shows that when the Bayes formula and certain game theory concepts are appropriately applied, the posterior probability of winning a prize in the famous Monty Hall game is not the counterintuitive answer of 2/3 but it is . The moral of this exercise is that the Bayes formula should
Panel Asymptotics and Statistical Decision Theory*
, 2015
"... This paper develops some applications of asymptotic statistical decision theory in econometrics, focusing on settings where the data are organized into groups or cells with heterogeneous parameters. Even if the groups are of different sizes, local asymptotic normality holds under suitable regulari ..."
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This paper develops some applications of asymptotic statistical decision theory in econometrics, focusing on settings where the data are organized into groups or cells with heterogeneous parameters. Even if the groups are of different sizes, local asymptotic normality holds under suitable
Decision making, movement planning and statistical decision theory
, 2008
"... We discuss behavioral studies directed at understanding how probability information is represented in motor and economic tasks. By formulating the behavioral tasks in the language of statistical decision theory, we can compare performance in equivalent tasks in different domains. Subjects in traditi ..."
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Cited by 41 (9 self)
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We discuss behavioral studies directed at understanding how probability information is represented in motor and economic tasks. By formulating the behavioral tasks in the language of statistical decision theory, we can compare performance in equivalent tasks in different domains. Subjects
Evolving Neural Networks for Statistical Decision Theory
, 2005
"... Real biological networks are able to make decisions. We will show that this behavior can be observed even in some simple architectures of biologically plausible neural models. The great interest of this thesis is also to contribute to methods of statistical decision theory by giving a lead how to ev ..."
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Cited by 1 (1 self)
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Real biological networks are able to make decisions. We will show that this behavior can be observed even in some simple architectures of biologically plausible neural models. The great interest of this thesis is also to contribute to methods of statistical decision theory by giving a lead how
Statistical Decision Theory Methods for Noise and Vibration Engineering
"... Statistical decision theory provides the noise and vibration engineer with the tools necessary to automate the decision process in practical applications such as automatic classification of acoustic events or faultdetection by vibroacoustic methods. We review the basic principles of statistical de ..."
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Statistical decision theory provides the noise and vibration engineer with the tools necessary to automate the decision process in practical applications such as automatic classification of acoustic events or faultdetection by vibroacoustic methods. We review the basic principles of statistical
Statistical Decision Theory for Mobile Robotics: Theory and Application
 Proc. of IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, Dec 811, Washington DC
, 1996
"... In this paper we pioneer a method which, given an input of mobile robot pose measurements by a sensorbased localization algorithm, produces a minimax risk fixedsize confidence set estimate for the pose of the agent. This work constitutes the first application to the mobile robotics domain of optim ..."
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Cited by 11 (6 self)
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of optimal fixedsize confidenceinterval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with the mobile agent. The method is compared to several other procedures including the Kalman Filter (minimum mean
Statistical Decision Theory for Mobile Robotics: Theory and Application
 Proc. of IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, Dec 811, Washington DC
, 1996
"... In this paper we pioneer a method which, given an input of mobile robot pose measurements by a sensorbased localization algorithm, produces a minimax risk fixedsize confidence set estimate for the pose of the agent. This work constitutes the first application to the mobile robotics domain of optima ..."
Abstract
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of optimal fixedsize confidenceinterval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with the mobile agent. The method is compared to several other procedures including the Kalman Filter (minimum mean
Sensitivity Analysis in Statistical Decision Theory: A Decision Analytic View
, 1995
"... We provide a framework for sensitivity analysis in statistical decision theory. The framework explores the effects of perturbations in both beliefs and preferences in the output of the analysis. It provides guidance on how to continue an analysis. We emphasize computational aspects. Keywords: Ba ..."
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Cited by 1 (0 self)
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We provide a framework for sensitivity analysis in statistical decision theory. The framework explores the effects of perturbations in both beliefs and preferences in the output of the analysis. It provides guidance on how to continue an analysis. We emphasize computational aspects. Keywords
MinimumRisk Profiles of Protein Families Based on Statistical Decision Theory
"... Statistical decision theory provides a principled way to estimate amino acid frequencies in conserved positions of a protein family. The goal is to minimize the risk function, or the expected squarederror distance between the estimates and the true population frequencies. The minimumrisk estima ..."
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Statistical decision theory provides a principled way to estimate amino acid frequencies in conserved positions of a protein family. The goal is to minimize the risk function, or the expected squarederror distance between the estimates and the true population frequencies. The minimum
Results 1  10
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45,772