EM experiment #3: blue class, scalar covariances

Number of Gaussians, assumes scalar covariances

Choose number of Gaussians in mixture model

Assumes scalar covariances

Compute EM algorithm

RowBox[{Timing, [, RowBox[{RowBox[{em, , =, , RowBox[{EM, [, RowBox[{data2, ,, initScalar, ,, 0.001}], ]}]}], ;}], ]}]

RowBox[{{, RowBox[{RowBox[{0.28, , Second}], ,, Null}], }}]

Visualization

Initial model

AnnularAllPlot[c1, rr1, rr2, em//First] ;

[Graphics:../HTMLFiles/index_47.gif]

Final model

AnnularAllPlot[c1, rr1, rr2, em//Last] ;

[Graphics:../HTMLFiles/index_49.gif]

Plot log-likelihood of data given the model as a function of EM iteration

PlotLogLikelihood[em, data2] ;

[Graphics:../HTMLFiles/index_51.gif]

Save solution

Created by Mathematica  (September 8, 2003)