log_post_params

evaluate the likelihood given the data, the current deterministic model solution and the observation model

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

log_post_params function

<dl><dt>samp</dt>
<dd>named numeric; current sample</dd>
<dt>w.p</dt>
<dd>character; parameter names</dd>
<dt>data</dt>
<dd>data</dd>
<dt>pdfs</dt>
<dd>character, prior pdf names</dd>
<dt>hyper</dt>
<dd>list, hyper parameters for the priors</dd>
<dt>sim.data</dt>
<dd>solver output</dd>
<dt>obs.model</dt>
<dd>function containing the observation model</dd></dl>

Arguments

log_post_params — log_post_params

<dl>

<dt>samp</dt>
<dd>named numeric; current sample</dd>


<dt>w.p</dt>
<dd>character; parameter names</dd>


<dt>data</dt>
<dd>data</dd>


<dt>pdfs</dt>
<dd>character, prior pdf names</dd>


<dt>hyper</dt>
<dd>list, hyper parameters for the priors</dd>


<dt>sim.data</dt>
<dd>solver output</dd>


<dt>obs.model</dt>
<dd>function containing the observation model</dd>

</dl>

log_post_params: log_post_params

Description

Usage

Arguments