MTB > # Chapter 1 - Example 2
MTB > # Bag contains one white ball.  Die is rolled; if the roll is i,
MTB > # then i red balls are put in bag.
MTB > # Bag is shaken and 1 ball is drawn.  Observe a red.
MTB > # Here there are 6 models:  '1 red',...,'6 red'
MTB > # There are two possible data outcomes:  'r' or 'w'
MTB > 
MTB > exec 'bayes_se'

INPUT NUMBER OF MODELS:
DATA> 6
 
INPUT NAMES OF MODELS (ONE NAME ON EACH LINE):
DATA> one red
DATA> two red
DATA> three red
DATA> four red
DATA> five red
DATA> six red
 
INPUT PRIOR PROBABILITIES OF MODELS:
DATA> .167 .167 .167 .167 .167 .167

WARNING:
  ************************************                                  
  INVALID PRIOR PROBABILITIES                                           
  (WILL NORMALIZE THEM IN CALCULATION)                                  
  ************************************                                  

INPUT THE NUMBER OF POSSIBLE OUTCOMES:
DATA> 2
 
INPUT THE NAME OF EACH OBSERVATION:
 (ONE OBSERVATION ON A LINE)
DATA> r
DATA> w
 
INPUT LIKELIHOODS OF EACH MODEL:

MODEL   
   1 

DATA> .5 .5

MODEL   
   2 

DATA> .667 .333

MODEL   
   3 

DATA> .75 .25

MODEL   
   4 

DATA> .8 .2

MODEL   
   5 

DATA> .833 .167

MODEL   
   6 

DATA> .857 .143
 
OBSERVATION NAMES:

 Row            OBS  OBS_NAME
   1   OUT_1            r    
   2   OUT_2            w    

TABLE OF PROBABILITIES OF MODELS AND OUTCOMES:

 Row  MODEL           NAME      PRIOR   OUT_1   OUT_2

   1      1   one red        0.166667   0.500   0.500
   2      2   two red        0.166667   0.667   0.333
   3      3   three red      0.166667   0.750   0.250
   4      4   four red       0.166667   0.800   0.200
   5      5   five red       0.166667   0.833   0.167
   6      6   six red        0.166667   0.857   0.143

MTB > exec 'bayes'

INPUT NUMBER OF OBSERVATIONS:
DATA> 1
 
INPUT OBSERVATIONS:
 (ONE OBSERVATION NAME ON A LINE:)
DATA> r

OUTCOME 
  r    

 Row  MODEL           NAME      PRIOR    LIKE    PRODUCT       POST
   1      1   one red        0.166667   0.500   0.083333   0.113456
   2      2   two red        0.166667   0.667   0.111167   0.151350
   3      3   three red      0.166667   0.750   0.125000   0.170184
   4      4   four red       0.166667   0.800   0.133333   0.181529
   5      5   five red       0.166667   0.833   0.138833   0.189017
   6      6   six red        0.166667   0.857   0.142833   0.194463

SUMMARY OF PRIOR AND POSTERIOR MODEL PROBABILITIES:

 Row  OBS_NO  OUTCOMES    PROB_M1    PROB_M2    PROB_M3    PROB_M4    PROB_M5

   1       0             0.166667   0.166667   0.166667   0.166667   0.166667
   2       1     r       0.113456   0.151350   0.170184   0.181529   0.189017

PROB_M6 
  0.166667   0.194463