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The algorithm is based on sequential approximate optimization and exploits some of the advantages of quadratic approximations while minimizing storage requirements.
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An approximate Bayesian computation (ABC) scheme based on sequential Monte Carlo (SMC) has been developed for likelihood-free parameter inference in deterministic and stochastic systems (Toni et al., 2009).
The algorithm proposed in this paper is based on sequential Monte Carlo methods which offer a flexible framework to approximate such distributions with weighted empirical measures associated with random samples.
PF is a sequential Monte Carlo methodology based on sequential importance sampling (SIS).
It uses Matlab software based on sequential running time.
Most of the solutions are based on sequential methods that make inference according to current information.
The GB conversion method is based on sequential Bayesian tracking, using a GB formulation.
Most traditional computational methods to classify SAPs are based on sequential or structural features.
Asterisks denote significance based on sequential Bonferroni correction critical values.
These correlations however are not significant after correction for multiple tests based on sequential Bonferroni [ 70].
Values in bold indicate correlations that remain significant after Bonferroni correction based on sequential Bonferroni tests.
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