ESD  MAIN# MAIN# MAIN0001 ESD  AFBVIOFM AFBVIOFM MAIN0002 ESD  AFBFIXPI AFBFIXPI MAIN0003 TXT  00MAIN# 93.33609.42.46 MAIN0004 TXT  } q0&}&}L2 MAIN0005 TXT}   MARGINAL PROB.VECTOR: CLASS COVARMAIN0006 TXT  IANCE MATRICES  (DIVISORS ARE ONE LESS THAN NUMBER IN MAIN0007 TXT  GROUP):COVARIANCE MATRIX FOR CLASS NO MAIN0008 TXT  OBSERVATIONS IN GROUP . STOPNUMBER OF PARAMEMAIN0009 TXT  TERS = SCHWARZ CRITERION FOR MODEL WITH DIFFERMAIN0010 TXTY  ENT COVARIANCE MATRICES =  AIC FOR MODEL WITHMAIN0011 TXT   DIFFERENT COVARIANCE MATRICES =  MAXIMUM FOR MAIN0012 TXT  EACH CHANNEL: MINIMUM FOR EACH CHANNEL: MAIN0013 TXT  MODEL WITH COMMON COVARIANCE MATRIX IS NOTOPTIMIZEDMAIN0014 TXTH   IN THIS PROGRAM; HOWEVER, IT IS CLEARTHAT ONE SHOUMAIN0015 TXT  LD USE IT IF HERE THE VALUES OF THE MODEL-SELECTIONMAIN0016 TXT   CRITERIA FOR THAT MODEL ARE LESS THAN THOSE FOR THMAIN0017 TXT  E MODEL WITH DIFFERENT COVARIANCE MATRICES,WHICH ISMAIN0018 TXTy   OPTIMIZED HERE.CLASS  CONTAINS ONLY  OMAIN0019 TXT\  BSERVATIONS: PRECISION MATRIX FROM PREVIOUS ITERATIONMAIN0020 TXT   IS BEING RETAINEDFOR MODEL WITH COMMON COVARIAMAIN0021 TXT&  NCE MATRIX (NOT OPTIMIZED IN THIS PROGRAM): MAIN0022 TXTh  SCHWARZ CRITERION WITH NEW LABELS AND NEW DISTRIBUTIOMAIN0023 TXT{  NAL PARAMETERS:  AIC WITH NEW LABELS AND NEW DMAIN0024 TXT8  ISTRIBUTIONAL  PARAMETERS:  SCHWARZ CRITERIONMAIN0025 TXT    WITH NEW LABELS AND OLD DISTRIBUTIONAL PARAMETERS: MAIN0026 TXT    AIC WITH NEW LABELS AND OLD DISTRIBUTIONAL  PMAIN0027 TXT  ARAMETERS:  PROGRAM ENDED SUCCESSFULLY FOR THEMAIN0028 TXT Q   CASE K = .ROUTINE HAS NOT CONVERGED IN 20 MAIN0029 TXT  ITERATIONS. STOPJFLG = . IF JFLG=0, COMPUTAMAIN0030 TXT  TION OF DET WENT WELL; OTHERWISE, THERE WAS TROUBLE ORMAIN0031 TXT   MATRIX WAS ILL-CONDITIONED.PROGRAM ENDED SUCMAIN0032 TXT  CESSFULLY.CONVERGENCE: NO CASE CHANGED CLASSES AMAIN0033 TXT0  FTER ITERATION . SOME ADDITIONAL RESULTS ARE PRINMAIN0034 TXT  TED BELOW.TRANSITION PROBABMAIN0035 TXT-  ILITIESNUMBEMAIN0036 TXTq  R OF COLUMNS = NUMBER OF ROWS =  CONTRIMAIN0037 TXT}  BUTION OF TRANS. PROBS. TO LOG LIKELIHOOD =   MAIN0038 TXT   COMMON COVARIANCE MATRIX (DIVISOR IS DF):MAIN0039 TXT  NUMBERS IN CLASSES: MMAIN0040 TXT  EAN VECTOR FOR CLASS :  ITERATION MAIN0041 TXT  MINUS 2 LOG LIKELIHOOD FOR MODEL WITH DIFFEREMAIN0042 TXTY  NT COVARIANCE MATRICES = FOR MODEL WITH DMAIN0043 TXT  IFFERENT COVARIANCE MATRICES:MINUS 2 LOG LIKELIMAIN0044 TXT  HOOD FOR MODEL WITH COMMON COVARIANCE MATRIX= MAIN0045 TXT  MEAN VECTOR FOR CLASS :  INITIAL MAIN0046 TXTH  MEAN VECTOR FOR CLASS :   1K =  CLMAIN0047 TXT  ASSES1MIN AND MAX NUMBER OF CLASSES TO BE TRMAIN0048 TXT  IED ARE  AND 1############################MAIN0049 TXT  #######################PROGRAM IMSEG.DET.AUTO MAIN0050 TXTy  FOR IMAGE SEGMENTATION USING DISTANCE IN THE MMAIN0051 TXT\  ETRICS OF THE COVARIANCE MATRICESADJUSTED BY THE DEMAIN0052 TXT   TERMINANTS DEVELOPED AMAIN0053 TXT &  ND PROGRAMMED BY  DR. STANLEY L. 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