post_sim

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

post_sim function

<dl><dt>x</dt>
<dd>debinfer_result object</dd>
<dt>n</dt>
<dd>number of simulations</dd>
<dt>times</dt>
<dd>numeric a vector of times at which the ODE is to be evaluated. Defaults to NULL.</dd>
<dt>output</dt>
<dd>character, "sims", "all", "HDI"</dd>
<dt>burnin</dt>
<dd>integer, number of samples to discard from the start of the mcmc chain</dd>
<dt>prob</dt>
<dd>A numeric scalar in the interval (0,1) giving the target probability content of the intervals. The nominal probability content of the intervals is the multiple of 1/nrow(obj) nearest to prob.</dd>
<dt>...</dt>
<dd>additional arguments to solver</dd></dl>

Arguments

post_sim — post_sim

<dl>

<dt>x</dt>
<dd>debinfer_result object</dd>


<dt>n</dt>
<dd>number of simulations</dd>


<dt>times</dt>
<dd>numeric a vector of times at which the ODE is to be evaluated. Defaults to NULL.</dd>


<dt>output</dt>
<dd>character, "sims", "all", "HDI"</dd>


<dt>burnin</dt>
<dd>integer, number of samples to discard from the start of the mcmc chain</dd>


<dt>prob</dt>
<dd>A numeric scalar in the interval (0,1) giving the target probability content of the intervals. The nominal probability content of the intervals is the multiple of 1/nrow(obj) nearest to prob.</dd>


<dt>...</dt>
<dd>additional arguments to solver</dd>

</dl>

post_sim: post_sim

Description

Usage

Value

Arguments