Important features of this method are that good results can be found even in very large search spaces and without prior assumptions, that the method is robust ...
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Specifically, we apply a Monte Carlo sampling method, based on likelihood-free Bayesian sampling, to traverse large parameter spaces, based on higher ...
May 10, 2018 · Specifically, we apply a Monte Carlo sampling method, based on likelihood-free Bayesian sampling, to traverse large parameter spaces, based on ...
In this paper, we extend the repertoire of static interface analysis to derive service variants, whereby subsets of operation parameters correspond to multiple ...
The research contributes to identifying key aspects of both the structure and behaviour of APIs, which will lead to building a simplified but comprehensive ...
May 1, 2018 · Rasmussen, Rune, Barros, Alistair, & Wei, Fuguo. (2018). A likelihood-free Bayesian derivation method for service variants. Journal of Systems ...
A likelihood-free Bayesian derivation method for service variants. J. Syst ... An approximate Bayesian computation approach for estimating parameters ...
Aug 18, 2024 · We introduce LF-GO-OED (likelihood-free goal-oriented optimal experimental design), a computational method for conducting GO-OED with nonlinear ...
The general form of a likelihood-free algorithm is to first propose a value for the parameter of interest. Second, one simulates the model of interest many ...
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Missing: derivation service variants.