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Fast Approximate Stochastic Tractography
Neuroinformatics, 2011Many different probabilistic tractography methods have been proposed in the literature to overcome the limitations of classical deterministic tractography: (i) lack of quantitative connectivity information; and (ii) robustness to noise, partial volume effects and selection of seed region.
Juan Eugenio Iglesias +3 more
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On approximate stochastic realization
Mathematics of Control, Signals, and Systems, 1991zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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1996
This chapter deals with algorithms for the optimization of simulated systems.In particular we study stochastic variants of the gradient algorithm xn+1=xn−an∇F(xn)] which was introduced in (1.27) to solve the optimization problem [F(x)=∥∥∥MinimizeF(x)x∈Rd] where F is bounded from below.
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This chapter deals with algorithms for the optimization of simulated systems.In particular we study stochastic variants of the gradient algorithm xn+1=xn−an∇F(xn)] which was introduced in (1.27) to solve the optimization problem [F(x)=∥∥∥MinimizeF(x)x∈Rd] where F is bounded from below.
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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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On the Lock-in Probability of Stochastic Approximation
Combinatorics, Probability and Computing, 2002For a stochastic approximation-type recursion with finitely many possible limit points, we find a lower bound on the probability of converging to a prescribed point in its ‘domain of attraction’. This has implications for the lock-in phenomena in the stochastic models of increasing return economics and the sample complexity of stochastic ...
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Stochastic Approximation, with Applications
2003Optimization is ubiquitous in various research and application fields. Many theoretical and practical problems are often reduced to optimizing some function L(·), i.e., finding its minimum (or maximum).
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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.
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