Likelihood System
Overview
For performing Bayesian inference using MCMC techniques, a likelihood function needs to be defined for measuring the quality of model predictions with reference to the experiments. The likelihood functions can be defined in the [Likelihood] block.
Creating a Likelihood Function
A likelihood function is created by inheriting from LikelihoodFunctionBase and ReporterInterface and overriding the function method in the base class. See the Gaussian class for an example.
Example Input File Syntax
[Likelihood<<<{"href": "index.html"}>>>]
[gaussian]
type = Gaussian<<<{"description": "Gaussian likelihood function evaluating the model goodness against experiments.", "href": "../../source/likelihoods/Gaussian.html"}>>>
noise<<<{"description": "Experimental noise plus model deviations against experiments."}>>> = 'noise_specified/noise_specified'
file_name<<<{"description": "Name of the CSV file with experimental values."}>>> = 'exp1.csv'
log_likelihood<<<{"description": "Compute log-likelihood or likelihood."}>>> = true
[]
[]Available Objects
- Stochastic Tools App
- ExtremeValueGeneralized extreme value likelihood function evaluating the model goodness against experiments.
- GaussianGaussian likelihood function evaluating the model goodness against experiments.
- TruncatedGaussianTruncatedGaussian likelihood function evaluating the model goodness against experiments.
Available Actions
- Stochastic Tools App
- AddLikelihoodActionAdds Likelihood objects.