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An iterative parameter estimation method for biological systems and its parallel implementation
Author(s) -
Yang Xian,
Guo Yike,
Guo Li
Publication year - 2013
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3071
Subject(s) - computation , monte carlo method , approximate bayesian computation , computer science , algorithm , sampling (signal processing) , estimation theory , parameter space , importance sampling , bayesian probability , mathematical optimization , process (computing) , markov chain monte carlo , speedup , mathematics , artificial intelligence , statistics , parallel computing , filter (signal processing) , inference , computer vision , operating system
SUMMARY One difficulty in building a mechanistic model of biological systems lies in determining correct parameter values. This paper proposes a novel parameter estimation method to infer unknown parameters, such as kinetic rates, from noisy experimental observations. Derived from the approximate Bayesian computation sequential Monte Carlo algorithm, our method predicts the distribution of each parameter rather than a single value via several intermediate distributions. Motivated by the computational intensity of the method, we improve the approximate Bayesian computation sequential Monte Carlo method in two aspects. First, to increase the efficiency, a windowing method is developed to reduce the parameter‐searching space, and an adaptive sampling weight mechanism is introduced to make the intermediate distributions converge to the target distributions in a much quicker manner. Second, to speed up the estimation process, we implement our method in a parallel computing environment to speed up the sampling process. Copyright © 2013 John Wiley & Sons, Ltd.