Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems.

ABSTRACT:

Background

Parameter estimation for differential equation models of intracellular processes is a highly relevant bu challenging task. The available experimental data do not usually contain enough information to identify all parameters uniquely, resulting in ill-posed estimation problems with often highly correlated parameters. Sampling-based Bayesian statistical approaches are appropriate for tackling this problem. The samples are typically generated via Markov chain Monte Carlo, however such methods are computationally expensive and their convergence may be slow, especially if there are strong correlations between parameters. Monte Carlo methods based on Euclidean or Riemannian Hamiltonian dynamics have been shown to outperform other samplers by making proposal moves that take the

SUBMITTER: Kramer A 

PROVIDER: S-EPMC4262080 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature