Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Calibrate hyperparameter multipliers on the observation error covariance
Alias: none
Argument(s): none
Default: none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Required (Choose One)  Group 1  one  Calibrate one hyperparameter multiplier across all responses/experiments  
per_experiment  Calibrate one hyperparameter multiplier per experiment  
per_response  Calibrate one hyperparameter multiplier per response  
both  Calibrate one hyperparameter multiplier for each response/experiment pair  
Optional  hyperprior_alphas  Shape (alpha) of the inverse gamma hyperparameter prior 
Calibrate one or more multipliers on the userprovided observation error covariance (variance_type). Options include one
multiplier on the whole blockdiagonal 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 hyperparameter calibration. When hyperparameter 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 hyperparameters.
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 hyperparameter 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 variance_type = 'scalar'