calculates the Maxiumum APosteriori value (MAP)

General-purpose MCMC and SMC samplers, as well as plot and
diagnostic functions for Bayesian statistics, with a particular focus on
calibrating complex system models. Implemented samplers include various
Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the
T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential
Monte Carlo (SMC) particle filter.

Florian Hartig

BayesianTools

General-Purpose MCMC and SMC Samplers and Tools for Bayesian
Statistics

Francesco Minunno

Stefan  Paul

David Cameron

Tankred Ott

Maximilian Pichler

MAP function

<dl><dt>bayesianOutput</dt>
<dd>an object of class BayesianOutput (mcmcSampler, smcSampler, or mcmcList)</dd>
<dt>...</dt>
<dd>optional values to be passed on the the getSample function</dd></dl>

Arguments

Author

calculates the Maxiumum APosteriori value (MAP) — MAP

<dl>

<dt>bayesianOutput</dt>
<dd>an object of class BayesianOutput (mcmcSampler, smcSampler, or mcmcList)</dd>


<dt>...</dt>
<dd>optional values to be passed on the the getSample function</dd>

</dl>

MAP: calculates the Maxiumum APosteriori value (MAP)

Description

Usage

Arguments

Author

Details

See Also