MTB > name c1 'model' c2 'prior1' c3 'prior2'
MTB > set 'model'
DATA> .2:.34/.02
DATA> end
MTB > set 'prior1'
DATA> .05 .05 .1 .25 .25 .15 .10 .05
DATA> set 'prior2'
DATA> .2 .2 .2 .15 .1 .05 .05 .05
DATA> end
MTB > prin c1-c3

 Row  model  prior1  prior2

   1   0.20    0.05    0.20
   2   0.22    0.05    0.20
   3   0.24    0.10    0.20
   4   0.26    0.25    0.15
   5   0.28    0.25    0.10
   6   0.30    0.15    0.05
   7   0.32    0.10    0.05
   8   0.34    0.05    0.05

MTB > exec 'mod_crit'
MTB > ##################################################################
MTB > #  MACRO 'MOD_CRIT'                                              #
MTB > #  (CHARACTER GRAPHICS VERSION)                                  #
MTB > # -------------------------------------------------------------- #
MTB > #  INFERENCE USING A FINITE COLLECTION OF MODELS.                #
MTB > #  AND TWO PRIOR DISTRIBUTIONS                                   #
MTB > #--------------------------------------------------------------- #
MTB > #  INPUT:  VALUES OF MODEL IN 'MODEL' AND PRIOR PROBABILITIES    #
MTB > #          IN COLUMNS 'PRIOR1' AND 'PRIOR2'                      #
MTB > #  OUTPUT: POSTERIOR PROBABILITIES CORRESPONDING TO 2 PRIORS     #
MTB > #          IN COLUMN 'POST1' AND 'POST2'                         #
MTB > #          BAYES FACTOR COMPARING TWO PRIORS                     #
MTB > ##################################################################
 
INPUT THE NUMBER OF THE LIKELIHOOD:
(1-Binomial P, 2-Normal M, 3-Poisson L, 4-Hypergeometric S,
 5-Discrete Uniform N, 6-Capture/Recapture N, 7-Exponential M)
DATA> 1

INPUT   
  (number of successes, number of failures)                        

DATA> 10 20

 Row  model  prior1  prior2      POST1      POST2

   1   0.20    0.05    0.20   0.015655   0.084360
   2   0.22    0.05    0.20   0.024472   0.131873
   3   0.24    0.10    0.20   0.069496   0.187250
   4   0.26    0.25    0.15   0.226925   0.183429
   5   0.28    0.25    0.10   0.275262   0.148333
   6   0.30    0.15    0.05   0.187430   0.084169
   7   0.32    0.10    0.05   0.133430   0.089878
   8   0.34    0.05    0.05   0.067331   0.090708

TYPE 'y' AND RETURN FOR SUMMARIES:
y
 
FOR FIRST SET OF PRIOR PROBABILITIES:
-------------------------------------

PRIOR MEAN OF MODELS:

MEAN    
  0.274 

POSTERIOR MEAN OF MODELS:

MEAN    
  0.283087 

FOR SECOND SET OF PRIOR PROBABILITIES:
-------------------------------------
 
PRIOR MEAN OF MODELS:

MEAN    
  0.247 

POSTERIOR MEAN OF MODELS:

MEAN    
  0.264901 

--------------------------------------------------------
 
BAYES FACTOR IN FAVOR OF FIRST SET OF PRIOR PROBABILITIES:

BAYES_F 
  1.34720 

 
TYPE 'y' AND RETURN TO SEE PLOTS:
y

PLOT OF POSTERIOR PROBABILITIES FOR FIRST PRIOR:

         -
     0.30+
         -                                 *
 PROB    -                                 *
         -                                 2
         -                         2       *
     0.20+                         2       2
         -                         2       *       2
         -                         *       2       2
         -                         2       *       3       *
         -                         2       2       2       3
     0.10+                         2       *       2       3
         -                         *       *       2       3
         -                 6       2       2       2       3       6
         -                 6       2       *       2       3       6
         - 8       +       6       2       2       2       3       6
     0.00+ +       9       3       *       *       2       2       3
           +---------+---------+---------+---------+---------+------MODELS  
       0.200     0.225     0.250     0.275     0.300     0.325


PLOT OF POSTERIOR PROBABILITIES FOR SECOND PRIOR:

         -                 *
    0.180+                 *       2
         -                 *       *
 PROB    -                 *       *
         -                 2       *       2
         -         *       *       2       2
    0.120+         2       *       *       *
         -         2       2       *       2
         -         2       *       2       *                       *
         - 2       2       *       *       2       2       3       2
         - 3       *       *       *       2       3       3       3
    0.060+ 3       2       2       2       *       3       2       3
         - 3       2       *       *       2       3       3       2
         - 2       2       *       *       *       2       3       3
         - 3       2       2       2       2       3       2       3
         - 3       2       *       *       2       3       3       2
    0.000+ 2       *       *       *       *       2       2       2
           +---------+---------+---------+---------+---------+------MODELS  
       0.200     0.225     0.250     0.275     0.300     0.325