debinfer_cov

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.

Philipp H Boersch-Supan

deBInfer

Bayesian Inference for Differential Equations

Leah R Johnson

Sadie J Ryan

debinfer_cov function

<dl><dt>var.names</dt>
<dd>names of the parameters that are to be proposed together</dd>
<dt>sigma</dt>
<dd>covariance matrix</dd>
<dt>name</dt>
<dd>name of the joint block</dd>
<dt>samp.type</dt>
<dd>character; type of sampler. currently only "rw" = Normal random walk is implemented for multivariate proposals</dd></dl>

Arguments

debinfer_cov — debinfer_cov

<dl>

<dt>var.names</dt>
<dd>names of the parameters that are to be proposed together</dd>


<dt>sigma</dt>
<dd>covariance matrix</dd>


<dt>name</dt>
<dd>name of the joint block</dd>


<dt>samp.type</dt>
<dd>character; type of sampler. currently only "rw" = Normal random walk is implemented for multivariate proposals</dd>

</dl>

debinfer_cov: debinfer_cov

Description

Usage

Value

Arguments