Results 261 to 270 of about 271,753 (311)
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A Stochastic Approximation Method
IEEE Transactions on Systems, Man, and Cybernetics, 1971A new algorithm for stochastic approximation has been proposed, along with the assumptions and conditions necessary for convergence. It has been proved by two different methods that the algorithm converges to the sought value in the mean-square sense and with probability one.
Naresh K. Sinha, Michael P. Griscik
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Comments on "A Stochastic Approximation Method"
IEEE Transactions on Systems, Man, and Cybernetics, 1972The results stated in the above paper1 concerning an approved stochastic approximation method are considered. Formulas for the variances of the estimates are derived, and it is found that, in fact, the new algorithm is inferior to previously suggested ones.
Michael A. Budin, Naresh K. Sinha
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Combining the Stochastic Counterpart and Stochastic Approximation Methods
Discrete Event Dynamic Systems, 1997Let \(\ell(v, \theta)=E_v\{L(Y,\theta)\}\) be the expected performance of a discrete event system (DES), where \(L\) is the sample performance driven by an input vector \(Y\) with a probability density function \(f(y, v)\) and \(\theta\) is a parameter of the sample performance.
Jean-Pierre Dussault +3 more
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Block Mirror Stochastic Gradient Method For Stochastic Optimization
Journal of Scientific Computing, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jinda Yang +3 more
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On stochastic methods for surface reconstruction
The Visual Computer, 2007In this article, we present and discuss three statistical methods for surface reconstruction. A typical input to a surface reconstruction technique consists of a large set of points that has been sampled from a smooth surface and contains un- certain data in the form of noise and outliers. We first present a method that filters out uncertain and redun-
Waqar Saleem +4 more
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Soft Comput., 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gabor Rebner +3 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gabor Rebner +3 more
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2022
This chapter looks into stochastic methods used to model different situations, which often involve using random numbers in one way or another. Methods like resampling and bootstrapping illustrate how flexible and widespread stochastic methods are. The main advantage of stochastic methods revolves around how it can allow working out useful stuff without
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This chapter looks into stochastic methods used to model different situations, which often involve using random numbers in one way or another. Methods like resampling and bootstrapping illustrate how flexible and widespread stochastic methods are. The main advantage of stochastic methods revolves around how it can allow working out useful stuff without
openaire +2 more sources
Segmentation of Stochastic Images With a Stochastic Random Walker Method
IEEE Transactions on Image Processing, 2012We present an extension of the random walker segmentation to images with uncertain gray values. Such gray-value uncertainty may result from noise or other imaging artifacts or more general from measurement errors in the image acquisition process. The purpose is to quantify the influence of the gray-value uncertainty onto the result when using random ...
Torben Pätz, Tobias Preusser
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A stochastic improvement method for stochastic programming
Computational Statistics & Data Analysis, 1992zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Morita, Hiroshi, Ishii, Hiroaki
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