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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Calibrate hyper-parameter multipliers on the observation error covariance
Alias: none
Argument(s): none
Default: none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Required (Choose One) | Calibrate Error Multipliers (Group 1) | one | Calibrate one hyper-parameter multiplier across all responses/experiments | |
per_experiment | Calibrate one hyper-parameter multiplier per experiment | |||
per_response | Calibrate one hyper-parameter multiplier per response | |||
both | Calibrate one hyper-parameter multiplier for each response/experiment pair | |||
Optional | hyperprior_alphas | Shape (alpha) of the inverse gamma hyper-parameter prior |
Calibrate one or more multipliers on the user-provided observation error covariance (experiment_variance_type). Options include one
multiplier on the whole block-diagonal covariance structure, one multiplier per_experiment
covariance block, one multiplier per_response
covariance block, or separate multipliers for each response/experiment pair (for a total of number experiments X number response groups).
Default Behavior: No hyper-parameter calibration. When hyper-parameter calibration is enabled, the default prior on the multiplier is a diffuse inverse gamma, with mean and mode approximately 1.0.
Expected Output: Final calibration results will include both inference parameters and one or more calibrated hyper-parameters.
Usage Tips: The per_response option can be useful when each response has its own measurement error process, but all experiments were gathered with the same equipment and conditions. The per_experiment option might be used when working with data from multiple independent laboratories.
Perform Bayesian calibration with 2 calibration variables and two hyper-parameter multipliers, one per each of two responses. The multipliers are assumed the same across the 10 experiments. The priors on the multipliers are specified using the hyperprior_alphas and hyperprior_betas keywords.
bayes_calibration queso samples = 1000 seed = 348 dram calibrate_error_multipliers per_response hyperprior_alphas = 27.0 hyperprior_betas = 26.0 variables uniform_uncertain 2 upper_bounds 1.e8 10.0 lower_bounds 1.e6 0.1 initial_point 2.85e7 2.5 descriptors 'E' 'w' responses calibration_terms = 2 calibration_data_file = 'expdata.withsigma.dat' freeform num_experiments = 10 experiment_variance_type = 'scalar'