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The following section, Literature Review, presents and discusses the available knowledge over various dimensions of EBFS (e.g., structure, content, sampling, implementation guidelines).
In a first phase, the variety in methods that are used for sampling, implementation of the interventions, measures and data analysis were mapped in a systematic literature review using Cochrane guidelines.
The model was estimating using the Gibbs sampling implementation of Markov Chain Monte Carlo (MCMC) [ 29], using the BUGS software program [ 30].
In that section, we validated the importance sampling method by comparing it against extensive standard Monte Carlo simulations, and we demonstrate the effectiveness of our importance sampling implementation.
This importance sampling implementation is comparable to the bias procedure described in [ 10, 11], except in the way in which the biasing of the scattering angle is implemented.
Yao and Wang [ 3] proposed an importance sampling implementation to speed up Monte Carlo simulations in time-domain OCT (TD-OCT) systems.
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Further details on sample design, including sampling framework and sample implementation, are provided in the basic survey reports by IIPS.
In Sec. 3, we show numerical results for the standard Monte Carlo method and for our importance sampling implementations with different parameters.
A sample implementation for a mechatronics curriculum at Technische Universität Dresden is given.
We evaluated a sample implementation of the algorithm on several large polygons.
We term such an approach Polymorphic Optimization and present a sample implementation as part of a multi-agent optimization system.
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