Dakota Reference Manual: model_evidence

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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model_evidence

Calculate model evidence (marginal likelihood of model) when using Bayesian methods

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | mc_approx | Calculate model evidence using a Monte Carlo sampling approach
| Optional | evidence_samples | The number of samples used in Monte Carlo approximation of the model evidence.
| Optional | laplace_approx | Calculate model evidence using the Laplace approximation

## Description

Model evidence is used in Bayesian model selection and model averaging. It is defined as the probability of the data given the model, and is calculated by averaging the likelihood of the model parameters over all values of the model parameters according to their prior distributions. In Dakota, one must calculate the model evidence separately for each model and perform the normalization to obtain the posterior model plausibility for each model.

Default Behavior

When specifying `model_evidence`, there are two methods of calculating it. One or both may be specified. They include the Monte Carlo approximation, given by `mc_approx` and the Laplace approximation, given by `laplace_approx`. `mc_approx` is the default approach.

Expected Output Currently, the model evidence will be printed in the screen output with prefacing text indicating if it is calculated by Monte Carlo sampling or the Laplace approximation.

Usage Tips