Dakota Reference Manual: probability_refinement

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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probability_refinement

Allow refinement of probability and generalized reliability results using importance sampling

## Topics

This keyword is related to the topics:

- reliability_methods

## Specification

Alias: sample_refinement

Argument(s): none

Default: no refinement

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Importance Sampling Approach (Group 1) | import | Importance sampling option for probability refinement
adapt_import | Importance sampling option for probability refinement
mm_adapt_import | Importance sampling option for probability refinement
| Optional | refinement_samples | Number of samples used to refine a probabilty estimate or sampling design.

## Description

The `probability_refinement` allows refinement of probability and generalized reliability results using importance sampling. If one specifies `probability_refinement`, there are some additional options. One can specify which type of importance sampling to use (`import`, `adapt_import`, or `mm_adapt_import`). Additionally, one can specify the number of refinement samples to use with `refinement_samples` and the seed to use with `seed`.

The `probability_refinement` density reweighting accounts originally was developed based on Gaussian distributions. It now accounts for additional non-Gaussian cases.