deBInfer:
Bayesian inference for dynamical models of biological systems in R
- Differential equations (DEs) are commonly used to model the temporal
evolution of biological systems, but statistical methods for comparing
DE models to data and for parameter inference are relatively poorly
developed. This is especially problematic in the context of biological
systems where observations are often noisy and only a small number of
time points may be available.
- Bayesian approaches offer a coherent framework for parameter
inference that can account for multiple sources of uncertainty, while
making use of prior information. We present deBInfer, an R package
implementing a Bayesian framework for parameter inference in DEs. This
approach offers a rigorous methodology for parameter inference as well
as modeling the link between unobservable model states and parameters,
and observable quantities.
- deBInfer provides templates for the DE model, the observation model
and data likelihood, and the model parameters and their prior
distributions. A Markov chain Monte Carlo (MCMC) procedure processes
these inputs to estimate the posterior distributions of the parameters
and any derived quantities, including the model trajectories. Further
functionality is provided to facilitate MCMC diagnostics and the
visualisation of the posterior distributions of model parameters and
trajectories.
- The templating approach makes deBInfer applicable to a wide range of
DE models and we demonstrate its application to ordinary and delay DE
models for population ecology.
For more information read our software paper or get
in touch with pboesu@gmail.com
Software development is supported by NSF
grant PLR-1341649.