Bayesian Inference for Differential Equations
de_mcmc
debinfer_cov
debinfer_par
Get starting/fixed values of DE initial values
Get starting/fixed values of DE parameters
is.debinfer_parlist
is.debinfer_result
log_post_params
log_prior_params
logd_prior
Pairwise posterior marginals
Plot inference outputs
Plot posterior trajectory
Plot posterior marginals and corresponding priors
post_sim
prior_draw_rev
propose_joint
propose_single_rev
Reshape posterior model solutions
setup_debinfer
solve_de
Summary of the inference results
update_sample_rev
A Bayesian framework for parameter inference in differential equations. This approach offers a rigorous methodology for parameter inference as well as modeling the link between unobservable model states and parameters, and observable quantities. 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.