Abstract
In this work we present an efficient method to tackle the problem of parameter inference for stochastic biological models. We develop a variant of the Particle Swarm Optimization algorithm by including Probabilistic Dependency statistical models to detect the parameter dependencies. This results in a more efficient parameter inference of the biological model.We test the Probabilistic Dependency- PSO on a well-known benchmark problem: the thermal isomerization of α-pinene
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Forlin, M., Slanzi, D., Poli, I. (2012). Combining Probabilistic Dependency Models and Particle Swarm Optimization for Parameter Inference in Stochastic Biological Systems. In: Gaol, F., Nguyen, Q. (eds) Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science. Advances in Intelligent and Soft Computing, vol 145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28308-6_60
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DOI: https://doi.org/10.1007/978-3-642-28308-6_60
Publisher Name: Springer, Berlin, Heidelberg
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