1#@ s*: Label=FastTest
2#@ *: DakotaConfig=HAVE_QUESO
3
4# The analysis_driver 'bayes_linear' is an analytic
5# test function that equals the sum of the input
6# variables.  That is, the response Y = sum(X1:Xd),
7# where d is the number of inputs.  Brian Williams
8# has several examples assuming different types of
9# distributions on the inputs and different measurement
10# errors in pages 93-102 and Appendix A of the CASL Report
11# "User Guidelines and Best Practices for CASL VUQ
12# Analysis using Dakota."  CASL-U-2014-0038-00.
13# By Brian Adams et al.
14
15
16environment
17        tabular_data
18
19method,
20       bayes_calibration queso
21          dram
22	  seed = 34785
23          chain_samples = 1000
24          calibrate_error_multipliers one
25          #hyperprior_alphas = 1000.0                   	#s2,#s3
26          #hyperprior_betas = 1.0				#s2,#s3
27	  # TODO: debug negative information gain on test s3
28          posterior_stats kl_divergence				#s0,#s1,#s2
29          #output debug
30
31variables,
32	uniform_uncertain = 1                                   #s0,#s2
33#	uniform_uncertain = 5                                   #s3
34	  upper_bounds   0.6                                    #s0,#s2
35	  lower_bounds   0.1                                    #s0,#s2
36#	  upper_bounds   0.6 0.6 0.6 0.6 0.6 	                #s3
37#	  lower_bounds   0.1 0.1 0.1 0.1 0.1                    #s3
38#       normal_uncertain = 1                                    #s1
39#         means = 0.6                                           #s1
40#         std_deviation = 0.1                                   #s1
41
42interface,
43        direct
44	  analysis_driver = 'bayes_linear' # This is a test function based on B. William's linear verification tests.
45
46responses,
47 	calibration_terms = 1
48        calibration_data_file = 'dakota_bayes_linear.1.dat'	#s0,#s1
49#        calibration_data_file = 'dakota_bayes_linear.2.dat'	#s2
50#        calibration_data_file = 'dakota_bayes_linear.5.dat'	#s3
51          freeform
52          num_experiments = 100
53          variance_type = 'scalar'
54	no_gradients
55	no_hessians
56
57