• Corpus ID: 88519368

# Optimizing Threshold - Schedules for Approximate Bayesian Computation Sequential Monte Carlo Samplers: Applications to Molecular Systems

@article{Silk2012OptimizingT,
title={Optimizing Threshold - Schedules for Approximate Bayesian Computation Sequential Monte Carlo Samplers: Applications to Molecular Systems},
author={Daniel Silk and Stefano Filippi and Michael P. H. Stumpf},
journal={arXiv: Computation},
year={2012}
}
• Published 11 October 2012
• Computer Science
• arXiv: Computation
The likelihood-free sequential Approximate Bayesian Computation (ABC) algorithms, are increasingly popular inference tools for complex biological models. Such algorithms proceed by constructing a succession of probability distributions over the parameter space conditional upon the simulated data lying in an $\epsilon$--ball around the observed data, for decreasing values of the threshold $\epsilon$. While in theory, the distributions (starting from a suitably defined prior) will converge…

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