propFun

Helper function to create proposal

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

propFun function

<dl><dt>case</dt>
<dd>Type of Twalk move. Either "walk", "traverse", "hop" or "blow"</dd>
<dt>Npar</dt>
<dd>number of parameters</dd>
<dt>pn1</dt>
<dd>Probability determining the number of parameters that are changed.</dd>
<dt>x</dt>
<dd>parameter vector of chain 1</dd>
<dt>x2</dt>
<dd>parameter vector of chain 2</dd>
<dt>beta</dt>
<dd>parameter for "traverse" move proposals.</dd>
<dt>aw</dt>
<dd>"walk" move proposal parameter.</dd></dl>

Arguments

Helper function to create proposal — propFun

<dl>

<dt>case</dt>
<dd>Type of Twalk move. Either "walk", "traverse", "hop" or "blow"</dd>


<dt>Npar</dt>
<dd>number of parameters</dd>


<dt>pn1</dt>
<dd>Probability determining the number of parameters that are changed.</dd>


<dt>x</dt>
<dd>parameter vector of chain 1</dd>


<dt>x2</dt>
<dd>parameter vector of chain 2</dd>


<dt>beta</dt>
<dd>parameter for "traverse" move proposals.</dd>


<dt>aw</dt>
<dd>"walk" move proposal parameter.</dd>

</dl>

propFun: Helper function to create proposal

Description

Usage

Arguments

References