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Therein, the solution is approximated using the downhill simplex method (a stochastic optimization method).
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The Monte-Carlo method uses a stochastic method to order trips by their probabilities, with more likely trips contributing more to (5).
In stochastic model-based denoising methods, a stochastic parametric model for a speech signal is used instead of a general waveform model.
Different from the conventional critical excitation methods, a stochastic response index is treated as the objective function to be maximized.
In contrast to the conventional critical excitation methods, a stochastic response index is treated in this paper as the objective function to be maximized.
Therefore, the SPSA method is a stochastic version of the steepest descent algorithm.
This paper aims at extending this method in a stochastic context.
Thus, Sakata (Sakata et al. 2012) discussed prediction of microstructural failure probability using Monte-Carlo simulation, the perturbation-based stochastic homogenization method and a stochastic multiscale stress analysis.
We base our method on a stochastic nature of the messages as they evolve during FG/SPA processing.
The KF is an optimal state estimation method for a stochastic signal [29] that estimates the state of a discrete-time controlled process,, with measurement data, where and are governed by the linear stochastic difference equations: (3).
Since the proposed method is a stochastic approach, therefore the average results are important.
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