Combining Probabilistic Dependency Models and Particle Swarm Optimization for Parameter Inference in Stochastic Biological Systems

  • Michele Forlin
  • Debora Slanzi
  • Irene Poli
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)


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


Particle Swarm Optimization Particle Swarm Optimization Algorithm Probabilistic Dependency Standard Particle Swarm Optimization Parameter Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.European Centre for Living TechnologyS. MarcoIT
  2. 2.Department of Environmental Sciences, Informatics and StatisticsUniversity Ca’Foscari of VeniceCannaregioIT

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