mcmcMultipleChains

internal

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

mcmcMultipleChains function

<dl><dt>bayesianSetup</dt>
<dd>Object of class "BayesianSetup"</dd>
<dt>settings</dt>
<dd>list with settings for sampler</dd>
<dt>sampler</dt>
<dd>character, either "Metropolis" or "DE"</dd></dl>

Arguments

Run multiple chains — mcmcMultipleChains

<dl>

<dt>bayesianSetup</dt>
<dd>Object of class "BayesianSetup"</dd>


<dt>settings</dt>
<dd>list with settings for sampler</dd>


<dt>sampler</dt>
<dd>character, either "Metropolis" or "DE"</dd>

</dl>

Run multiple chains

mcmcMultipleChains: Run multiple chains

Description

Usage

Value

Arguments