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Such a behavior provides faster particle exploitation, as now the entire swarm becomes a single stochastic hill-climber.
Several correlated uncertainty factors are combined into a single stochastic process, which allows for analytical (closed-form) solutions.
Both the probabilistic response and its sensitivities can be estimated from a single stochastic analysis, without requiring performance function gradients.
In the latter case, the hybrid model provides a mathematical formulation of excitable membranes that incorporates the exact Markovian dynamics of single stochastic ion channels.
The first detailed and sophisticated attempt at a bolder approach to defining randomness for a sequence with a single stochastic property was by von Mises (von Mises, 1957; von Mises, 1941).
We employ the same strategy as in the main analysis: we first estimate parameters on data generated from single stochastic stimuli, and then fix the response kernel and estimate the other parameters on data generated from stochastic stimulus mixture.
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In this paper, a class of single-input single-output stochastic nonlinear systems in strict-feedback form with a risk sensitive cost criterion is considered.
In this category, the observations are assumed independent and identically distributed (i.i.d) random variables drawn from a single parametrised stochastic source.
In this paper, we examine the performance of single point stochastic techniques and compare them to several problem specific algorithms that exist in the literature.
We present a polynomial time algorithm that addresses these issues in the context of the class of single controller stochastic games, providing the agent with near-optimal return.
All missing data in quantitative variables was replaced using a single imputation stochastic regression method [ 27].
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