Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems

Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems

Abstract Background In quantitative biology, mathematical models are used to describe and analyze biological processes. The parameters of these models are usually unknown and need to be estimated from experimental data using statistical methods. In particular, Markov chain Monte Carlo (MCMC) methods...

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Bibliographic Details
Main Authors: Benjamin Ballnus, Sabine Hug, Kathrin Hatz, Linus Görlitz, Jan Hasenauer, Fabian J. Theis
Format: Article
Language:English
Published: BioMed Central 2017-06-01
Series:BMC Systems Biology
Subjects:
Parameter estimation
Markov chain Monte Carlo
Sampling analysis
Benchmark collection
Ordinary differential equation
Systems biology
Online Access:http://link.springer.com/article/10.1186/s12918-017-0433-1

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http://link.springer.com/article/10.1186/s12918-017-0433-1

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