Tutorial 2 - Mixture model

In this tutorial we will build a model of the {X,Y} position data shown in the chart below.

The following concepts will be covered:

Mixture model chart

NOTE

Bayes Server must be installed, before starting this tutorial. An evaluation version can be downloaded from the Downloads page

Companion video (No Audio)

Creating the model

We will create a Bayesian network that is equivalent to a Mixture model (also known as a Cluster Model), as shown below.

Position mixture model

Add the nodes

Learning the distributions

We could now enter the distribution parameters for the nodes manually using the Distribution editor, however in this tutorial we will learn the parameter values from data.

For convenience, we will use Microsoft Excel as the data source, however another database can be substituted.

Adding a data connection

Note: You can skip this step, and instead use the pre-installed Tutorial data connection (Walkthrough Data in earlier versions).

Parameter learning

Visualizing the network distributions

Creating a Custom Query

Charting the distributions with data

Data

X Y
0.176502224 7.640580199
1.308020831 8.321963251
7.841271129 3.34044587
2.623799516 6.667664279
8.617288623 3.319091539
0.292639161 9.070469416
1.717525934 6.509707265
0.347388367 9.144193334
4.332228381 0.129103276
0.550570479 9.925610034
10.18819907 3.414009144
9.796154937 4.335498562
4.492011746 0.527572356
8.793496377 3.811848391
0.479689038 8.041976487
0.460045193 10.74481444
3.249955813 5.58667984
1.677468832 8.742639202
2.567398263 3.338528008
8.507535409 3.358378353
8.863647208 3.533757566
-0.612339597 11.27289689
10.38075113 3.657256133
9.443691262 3.561824026
1.589644185 7.936062309
7.680055137 2.541577306
1.047477704 6.382052946
0.735659679 8.029083014
0.489446685 11.40715477
3.258072314 1.451124598
0.140278917 7.78885888
9.237538442 2.647543473
2.28453948 5.836716478
7.22011534 1.51979264
1.474811913 1.942052919
1.674889251 5.601765101
1.30742068 6.137114076
6.957133145 3.957540541
10.87472856 5.598949484
1.110499364 9.241584372
7.233905739 2.322237847
7.474329505 2.920099189
0.455631413 7.356350266
1.234318558 6.592203772
10.72837103 5.371838788
0.655168407 6.713544957
2.001307579 5.30283356
0.061834893 2.071499561
1.86460938 6.013710897
9.35680964 3.719046646
-0.008787992 7.387352578
0.610918535 8.343425847
-0.238965542 9.89893411
1.940925093 6.209752266
1.333199057 7.59848403
8.484655224 3.073253305
1.364358184 5.975527829
10.72748994 4.134446075
2.046614845 7.437682289
1.662951156 6.370669577
3.162551343 4.864600865
2.789107868 6.143289172
2.587010436 1.599672084
1.470218845 8.656125114
1.409410007 0.992888942
0.919912218 7.052651078
8.778925691 3.704669502
1.117567765 1.993522613
-0.144489104 11.53479807
5.284863514 1.489314676
2.663178432 3.177481897
2.011776623 7.897365033
1.464680213 1.528483262
0.158678139 8.908835673
0.214401967 7.292093447
2.402088546 2.362154057
2.378733602 2.551873091
0.827701089 10.69624252
0.395016071 8.305645848
2.121369004 2.815448463
7.169453919 1.905806566
0.601520002 9.785279396
1.586490061 6.726857095
1.439861204 7.014361866
-0.686397699 9.77425866
0.801845144 6.804976671
1.137477302 0.225502899
1.921361226 1.7909808
5.173856001 0.823420025
1.05037942 9.453914186
1.111047436 2.742705875
2.474067445 2.812341558
6.804286117 1.95500379
0.819301796 11.32009105
2.617654071 2.264324097
1.027061887 8.046658989
6.149555065 1.891771827
8.034889849 3.017307147
-0.687339329 9.029022351
5.263367085 1.680024298
1.195130289 6.286967967
1.731321429 7.390609268
1.551609971 6.336140355
-0.520890154 10.19932065
3.428134061 4.078913686
2.572038859 3.089414526
-0.399780876 9.469230934
1.718614257 7.038834914
11.43348179 5.957011929
3.957769968 3.837668973
1.397437302 6.075260837
0.641022085 7.121565252
0.898241907 9.891320102
7.881545649 2.520490475
0.466133925 6.923223388
1.083456697 0.744882546
1.676386764 7.311645991
7.09954842 1.896511497
0.064081268 9.625098882
0.934196198 1.707061134
1.773186249 7.078639821
2.614429517 8.186884596
1.588726807 3.189121762
2.576481413 8.338793925
1.493343882 7.817329126
1.040380815 7.019325225
1.2238645 7.52380108
0.564538219 8.554700627
7.522790642 2.205814269
7.221158478 1.881945286
6.155353437 2.956359604
0.543960458 9.876015061
2.969310521 1.689421147
1.130952545 7.323147618
1.637814935 7.505231003
3.0319218 1.806998212
-0.691891787 10.68314634
-0.172458962 11.0038236
6.382030326 1.529850265
7.081234369 3.018901732
6.420440542 1.710179248
2.567996925 2.175798021
5.484764693 1.249988782
2.169826086 1.457485314
2.666166169 3.006020372
1.255748487 8.172890601
1.110450177 6.909645674
0.64221948 7.115968797
7.382062636 2.885279632
-0.390488356 10.58445538
8.673875474 4.606369236
2.703825246 4.532865095
0.256417369 8.637987542
2.171303599 2.887466856
0.76946757 9.931359151
1.429713914 7.061909133
1.916059822 0.411361527
2.069221406 1.169196508
2.443632587 1.633471641
8.651228033 3.478728796
5.094879523 0.618202099
1.197475474 7.806661104
2.721229947 -1.040833105
0.649135449 6.703355477
0.955899266 10.0812704
-0.05107945 9.412982102
2.09150178 7.85570867
8.496542476 2.72631079
6.129258907 1.391867166
1.415242321 8.69036834
-0.181799846 9.564270677
2.147903749 5.971313108
7.429093289 1.837920789
0.258858273 8.36201855
0.436279242 7.122238994
2.400524268 9.484131132
8.949800461 4.050725157
1.377808913 9.131672137
0.488721438 6.24375667
0.938826647 6.533751708
1.609019133 9.491402761
7.686040142 2.571497086
7.913477158 2.973634152
-0.141689656 7.490501119
1.54214829 1.462388521
8.836690062 3.323118698
1.292553241 7.696934647
1.338461668 0.916163751
2.223196493 7.092454045
7.283823688 2.739494961
3.118964374 5.500739786
0.807728186 6.844431805
0.670279272 10.92590148
9.12996622 3.917329879
3.546742416 1.337113351
0.766419935 7.261302999
12.55210397 5.948973499
0.376685504 9.865645387
0.890836141 6.556401491
7.597140488 1.163621719