Dakota Reference Manual: posterior_adaptive

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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posterior_adaptive

Adapt emulator model to increase accuracy in high posterior probability regions

## Specification

Alias: none

Argument(s): none

## Description

Following an emulator-based MCMC process, this option refines the emulator by selecting points in regions of high posterior probability, performing truth evaluations at these points, updating the emulator, and reperforming the MCMC process. The adaptation is continued until the maximum number of iterations is exceeded or the convergence tolerance is met.

## Examples

    bayes_calibration queso
      chain_samples = 2000 seed = 348
      delayed_rejection
      emulator
        gaussian_process surfpack build_samples = 30
        posterior_adaptive max_iterations = 10
        proposal_covariance derivatives