MTB > name c1 'model' c2 'prior' MTB > set 'model' DATA> 0:100 DATA> end MTB > let 'prior'=0*'model'+1/101 MTB > exec 'mod_disc' MTB > ################################################################## MTB > # MACRO 'MOD_DISC' # MTB > # (CHARACTER GRAPHICS VERSION) # MTB > # -------------------------------------------------------------- # MTB > # INFERENCE USING A FINITE COLLECTION OF MODELS. # MTB > #--------------------------------------------------------------- # MTB > # INPUT: VALUES OF MODEL IN 'MODEL' AND PRIOR PROBABILITIES # MTB > # IN COLUMN 'PRIOR' # MTB > # OUTPUT: POSTERIOR PROBABILITIES IN COLUMN 'POST' # 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> 4 INPUT (population size, sample size, number of successes) DATA> 100 20 12 Row model prior LIKE PRODUCT POST 1 0 0.009901 0 0.00 0.0000000 2 1 0.009901 0 0.00 0.0000000 3 2 0.009901 0 0.00 0.0000000 4 3 0.009901 0 0.00 0.0000000 5 4 0.009901 0 0.00 0.0000000 6 5 0.009901 0 0.00 0.0000000 7 6 0.009901 0 0.00 0.0000000 8 7 0.009901 0 0.00 0.0000000 9 8 0.009901 0 0.00 0.0000000 10 9 0.009901 0 0.00 0.0000000 11 10 0.009901 0 0.00 0.0000000 12 11 0.009901 0 0.00 0.0000000 13 12 0.009901 0 0.00 0.0000000 14 13 0.009901 0 0.00 0.0000000 15 14 0.009901 0 0.00 0.0000000 16 15 0.009901 0 0.00 0.0000000 17 16 0.009901 1 0.01 0.0000000 18 17 0.009901 2 0.02 0.0000001 19 18 0.009901 6 0.06 0.0000003 20 19 0.009901 15 0.15 0.0000006 21 20 0.009901 34 0.34 0.0000014 22 21 0.009901 72 0.71 0.0000030 23 22 0.009901 141 1.40 0.0000059 24 23 0.009901 265 2.63 0.0000111 25 24 0.009901 475 4.70 0.0000198 26 25 0.009901 817 8.09 0.0000341 27 26 0.009901 1355 13.42 0.0000566 28 27 0.009901 2175 21.54 0.0000908 29 28 0.009901 3389 33.55 0.0001414 30 29 0.009901 5137 50.86 0.0002144 31 30 0.009901 7596 75.21 0.0003171 32 31 0.009901 10976 108.67 0.0004581 33 32 0.009901 15522 153.69 0.0006479 34 33 0.009901 21521 213.08 0.0008983 35 34 0.009901 29286 289.96 0.0012224 36 35 0.009901 39159 387.71 0.0016345 37 36 0.009901 51505 509.95 0.0021498 38 37 0.009901 66693 660.33 0.0027838 39 38 0.009901 85090 842.47 0.0035517 40 39 0.009901 107043 1059.83 0.0044680 41 40 0.009901 132862 1315.46 0.0055457 42 41 0.009901 162783 1611.71 0.0067946 43 42 0.009901 196990 1950.39 0.0082225 44 43 0.009901 235547 2332.15 0.0098318 45 44 0.009901 278407 2756.51 0.0116209 46 45 0.009901 325411 3221.89 0.0135828 47 46 0.009901 376213 3724.88 0.0157033 48 47 0.009901 430338 4260.77 0.0179625 49 48 0.009901 487160 4823.37 0.0203343 50 49 0.009901 545885 5404.80 0.0227855 51 50 0.009901 605615 5996.19 0.0252787 52 51 0.009901 665240 6586.53 0.0277675 53 52 0.009901 723622 7164.57 0.0302044 54 53 0.009901 779502 7717.85 0.0325368 55 54 0.009901 831626 8233.93 0.0347125 56 55 0.009901 878708 8700.08 0.0366777 57 56 0.009901 919530 9104.26 0.0383817 58 57 0.009901 952999 9435.64 0.0397787 59 58 0.009901 978043 9683.60 0.0408241 60 59 0.009901 993923 9840.82 0.0414869 61 60 0.009901 1000000 9900.99 0.0417405 62 61 0.009901 995919 9860.59 0.0415702 63 62 0.009901 981639 9719.20 0.0409741 64 63 0.009901 957364 9478.86 0.0399609 65 64 0.009901 923559 9144.15 0.0385498 66 65 0.009901 880990 8722.68 0.0367730 67 66 0.009901 830701 8224.76 0.0346739 68 67 0.009901 773867 7662.05 0.0323016 69 68 0.009901 711925 7048.76 0.0297161 70 69 0.009901 646388 6399.88 0.0269806 71 70 0.009901 578831 5731.00 0.0241607 72 71 0.009901 510847 5057.90 0.0213231 73 72 0.009901 443947 4395.52 0.0185306 74 73 0.009901 379543 3757.85 0.0158423 75 74 0.009901 318811 3156.54 0.0133073 76 75 0.009901 262791 2601.89 0.0109691 77 76 0.009901 212234 2101.33 0.0088588 78 77 0.009901 167640 1659.81 0.0069974 79 78 0.009901 129240 1279.61 0.0053946 80 79 0.009901 96999 960.39 0.0040488 81 80 0.009901 70668 699.68 0.0029497 82 81 0.009901 49795 493.02 0.0020785 83 82 0.009901 33789 334.54 0.0014104 84 83 0.009901 21958 217.40 0.0009165 85 84 0.009901 13573 134.39 0.0005666 86 85 0.009901 7911 78.33 0.0003302 87 86 0.009901 4297 42.55 0.0001794 88 87 0.009901 2141 21.20 0.0000894 89 88 0.009901 957 9.48 0.0000400 90 89 0.009901 371 3.67 0.0000155 91 90 0.009901 118 1.17 0.0000049 92 91 0.009901 28 0.28 0.0000012 93 92 0.009901 1 0.01 0.0000001 94 93 0.009901 0 0.00 0.0000000 95 94 0.009901 0 0.00 0.0000000 96 95 0.009901 0 0.00 0.0000000 97 96 0.009901 0 0.00 0.0000000 98 97 0.009901 0 0.00 0.0000000 99 98 0.009901 0 0.00 0.0000000 100 99 0.009901 0 0.00 0.0000000 101 100 0.009901 0 0.00 0.0000000 PRIOR MEAN OF MODELS: MEAN 50 POSTERIOR MEAN OF MODELS: MEAN 59.2748 - 0.045+ - *22 PROB - *3323 - *433433 - 2232232* 0.030+ 244344343 - 4433223442 - 44432443344 - *443342233342 - 5553424334456 0.015+ 466442423334463 - 2874434222334669* - 2+866443444434458+ - 4++965443222244579++ - 9++++874444444444468+++6 0.000+ +++++++++++++++++++++975443222222234457++++++++++++ +---------+---------+---------+---------+---------+------MODELS 0 20 40 60 80 100