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 © 2012 Springer-Verlag GmbH.
CITATION STYLE
Forlin, M., Slanzi, D., & Poli, I. (2012). Combining probabilistic dependency models and particle swarm optimization for parameter inference in stochastic biological systems. In Advances in Intelligent and Soft Computing (Vol. 145 AISC, pp. 437–443). https://doi.org/10.1007/978-3-642-28308-6_60
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