Specify probability levels at which to compute credible and prediction intervals


  • Alias: None

  • Arguments: REALLIST

  • Default: No CDF/CCDF response levels to compute

Child Keywords:


Description of Group

Dakota Keyword

Dakota Keyword Description



This child keyword is currently inactive


Credible and prediction intervals of model responses are computed for specified probabilities. Credible intervals are calculated from the response function values corresponding to the final MCMC chain. Calculation of prediction intervals consider these response values as well as the experimental uncertainty, which is specified by the user via the experiment_variance_type command.

Expected Output

If probability_levels is specified, Dakota will create a table containing the credibile intervals for each response function. The corresponding table containing the prediction intervals will also be created if a experiment_variance_type has been specified. This information is output to the screen and to a file. In addition, the output file contains the means and standard deviations of each response function and Gaussian approximations of the 5/95 credible and prediction intervals, in which the lower bound is two standard deviations below the mean and the upper bound is two standard deviations above the mean.

Usage Tips

Only one probability level needs to be specified for each desired interval. Both corresponding end points of the intervals are automatically calculated. For example, if 0.05 is specified, both the 0.05 and 0.95 probability levels are output to the screen and output file.

Additional Discussion

Credible intervals propagate uncertainties in parameter density information to the quantity of interest and quantify how well the model fits the provided data. Prediction intervals propagate both parameter and experimental measurement uncertainties and contain the next experimental or simulated observation with the specified probability.


Below is a Dakota input file specifying the calculation of credible and prediction intervals

 bayes_calibration queso
   chain_samples = 1000 seed = 348
   diagonal values 1.0e6 1.0e-1
 probability_levels 0.05  0.1
      0.075 0.1

 uniform_uncertain 2
          upper_bounds  1.e8 10.0
          lower_bounds 1.e6 0.1
          initial_point 2.85e7 2.5
          descriptors 'E' 'w'
        continuous_state 4
          initial_state 3 40000 500 1000
          descriptors 't' 'R' 'X' 'Y'

          analysis_driver = 'mod_cantilever'

        calibration_terms = 2
        calibration_data_file = 'dakota_cantilever_queso.withsigma.dat'
          num_experiments = 10
          experiment_variance_type = 'scalar'
        descriptors = 'stress' 'displacement'

The resulting screen output below shows the table of credible and prediction intervals.

Credibility Intervals for stress
                  Response Level    Probability Level
                  ----------------- -----------------
                  2.4764049695e+03  5.0000000000e-02
                  2.8242874802e+03  9.5000000000e-01
                  2.4990608791e+03  1.0000000000e-01
                  2.7952985803e+03  9.0000000000e-01
Credibility Intervals for displacement
                  Response Level    Probability Level
                  ----------------- -----------------
                  2.7409870925e-01  7.5000000000e-02
                  3.0991296255e-01  9.2500000000e-01
                  2.7538816802e-01  1.0000000000e-01
                  3.0889319332e-01  9.0000000000e-01
Prediction Intervals for stress
                  Response Level    Probability Level
                  ----------------- -----------------
                  2.0964882850e+03  5.0000000000e-02
                  3.1993026765e+03  9.5000000000e-01
                  2.1822183238e+03  1.0000000000e-01
                  3.1099058450e+03  9.0000000000e-01
Prediction Intervals for displacement
                  Response Level    Probability Level
                  ----------------- -----------------
                  2.3559036055e-01  7.5000000000e-02
                  3.5097481218e-01  9.2500000000e-01
                  2.4016170870e-01  1.0000000000e-01
                  3.4701712866e-01  9.0000000000e-01