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MCMC_CLIB-an advanced MCMC sampling package for ODE models.

Andrei Kramer1, Vassilios Stathopoulos1, Mark Girolami1

  • 1Institute for Systems Theory and Automatic Control, University of Stuttgart, 70569 Stuttgart, Germany, Department of Statistical Science, University College, London WC1E 6BT, UK and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

A new C implementation of Markov chain Monte Carlo (MCMC) sampling for ordinary differential equation (ODE) models is introduced. The software uses the Simplified Manifold Metropolis-Adjusted Langevin Algorithm (SMMALA) for efficient parameter estimation.

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Area of Science:

  • Computational biology
  • Statistical modeling
  • Numerical analysis

Background:

  • Ordinary differential equation (ODE) models are widely used in various scientific fields.
  • Parameter estimation for ODE models can be computationally challenging.
  • Advanced sampling methods are needed for complex model posteriors.

Purpose of the Study:

  • To present a new C implementation of an advanced Markov chain Monte Carlo (MCMC) method.
  • To provide efficient sampling of ordinary differential equation (ODE) model parameters.
  • To introduce the Simplified Manifold Metropolis-Adjusted Langevin Algorithm (SMMALA) for parameter estimation.

Main Methods:

  • Implementation of the Simplified Manifold Metropolis-Adjusted Langevin Algorithm (SMMALA) in C.
  • Utilizing the parameter manifold's geometry (Fisher information) for locally adaptive moves.
  • Integration with GNU Scientific Library and SUNDIALS for ODE integration and sensitivity analysis.

Main Results:

  • The SMMALA implementation offers efficient parameter sampling for ODE models.
  • The local adaptation of SMMALA does not diminish with Markov chain (MC) length.
  • This method is advantageous for parameters with large correlations or non-Gaussian posteriors.

Conclusions:

  • The developed C software, mcmc_clib, provides a standalone and efficient tool for ODE model parameter sampling.
  • SMMALA offers a robust alternative to traditional adaptive Metropolis techniques.
  • The software is freely available with comprehensive documentation and examples.