logd_prior

Evaluates the log probability density of value given a name of a prior pdf and the corresponding hyperparameters

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

logd_prior function

<dl><dt>x</dt>
<dd>numeric; vector of values.</dd>
<dt>pdf</dt>
<dd>character; name of a probability function. Must conform to base R nomenclature of d/r function pairs. Can include trunc for truncated pdfs from package truncdist.</dd>
<dt>hypers</dt>
<dd>list; a list of parameters to be passed to the density function.</dd></dl>

Arguments

logd_prior — logd_prior

<dl>

<dt>x</dt>
<dd>numeric; vector of values.</dd>


<dt>pdf</dt>
<dd>character; name of a probability function. Must conform to base R nomenclature of d/r function pairs. Can include trunc for truncated pdfs from package truncdist.</dd>


<dt>hypers</dt>
<dd>list; a list of parameters to be passed to the density function.</dd>

</dl>

logd_prior: logd_prior

Description

Usage

Value

Arguments