MCMC_CLIB-an advanced MCMC sampling package for ODE models

  • Kramer A
  • Stathopoulos V
  • Girolami M
 et al. 
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Abstract

SUMMARY:: We present a new C implementation of an advanced Markov chain Monte Carlo (MCMC) method for the sampling of ordinary differential equation (ode) model parameters. The software mcmc_clib uses the simplified manifold Metropolis-adjusted Langevin algorithm (SMMALA), which is locally adaptive; it uses the parameter manifold's geometry (the Fisher information) to make efficient moves. This adaptation does not diminish with MC length, which is highly advantageous compared with adaptive Metropolis techniques when the parameters have large correlations and/or posteriors substantially differ from multivariate Gaussians. The software is standalone (not a toolbox), though dependencies include the GNU scientific library and sundials libraries for ode integration and sensitivity analysis. Availability and implementation: The source code and binary files are freely available for download at http://a-kramer.github.io/mcmc_clib/. This also includes example files and data. A detailed documentation, an example model and user manual are provided with the software.

CONTACT:: andrei.kramer@ist.uni-stuttgart.de.

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Authors

  • Andrei Kramer

  • Vassilios Stathopoulos

  • Mark Girolami

  • Nicole Radde

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