likelihoodIidNormal

Normal / Gaussian Likelihood function

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

likelihoodIidNormal function

<dl><dt>predicted</dt>
<dd>vector of predicted values</dd>
<dt>observed</dt>
<dd>vector of observed values</dd>
<dt>sd</dt>
<dd>standard deviation of the i.i.d. normal likelihood</dd></dl>

Arguments

Author

Normal / Gaussian Likelihood function — likelihoodIidNormal

<dl>

<dt>predicted</dt>
<dd>vector of predicted values</dd>


<dt>observed</dt>
<dd>vector of observed values</dd>


<dt>sd</dt>
<dd>standard deviation of the i.i.d. normal likelihood</dd>

</dl>

likelihoodIidNormal: Normal / Gaussian Likelihood function

Description

Usage

Arguments

Author