Probabilistic Network
Library
User Guide and
Reference Manual
Copyright ©2002-2003 Intel Corporation
All Rights Reserved
Issued in U.S.A.
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Copyright
© 2002-2003 Intel Corporation.
Version Version History Date
-001 Original Issue July, 2002
-002 Changed the title: the former title “Probabilistic Graphical Models
Toolkit”; edited and amended User Guide; all prefixes in function
names changed from CPGM- and EPGM- to C- and E-
respectively; changed names of 8 classes; made numerous
changes in desription of functions and in examples; added 50%
new functions and operators.
February, 2003
-003 Major functionality updates December, 2003
-004 Functionality updates March, 2004
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Contents
Contents
Chapter 1 Overview
About This Library .................................................................................. 2-1
About This Software ............................................................................... 2-1
About This Manual ................................................................................. 2-2
Notational Conventions ......................................................................... 2-2
Font Conventions .............................................................................. 2-2
Naming Conventions ......................................................................... 2-2
Chapter 2 User Guide
Graphical Models ................................................................................... 3-1
Dynamic Graphical Models................................................................ 3-7
Inference Algorithms for Bayesian and Markov Networks.................... 3-10
Inference Algorithms for DBNs............................................................ 3-15
Learning for Bayesian and Markov Networks....................................... 3-21
Type 1 .............................................................................................. 3-22
Type 2 .............................................................................................. 3-26
Type 3 .............................................................................................. 3-29
Learning for DBNs................................................................................ 3-31
Log Subsystem..................................................................................... 3-33
Chapter 3 Reference Manual
Graph ..................................................................................................... 4-1
Class CGraph .................................................................................... 4-1
Class CDAG..................................................................................... 4-22
Probabilistic Network Library Contents
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Node Types........................................................................................... 4-34
Class CNodeType ............................................................................ 4-34
Model Domain ...................................................................................... 4-36
Class CModelDomain ...................................................................... 4-37
Evidences............................................................................................. 4-43
Class CNodeValues......................................................................... 4-43
Class CEvidence ............................................................................. 4-49
Graphical Models.................................................................................. 4-57
Class CGraphicalModel ................................................................... 4-57
Class CStaticGraphicalModel .......................................................... 4-65
Class CBNet .................................................................................... 4-67
Class CMNet.................................................................................... 4-72
Class CMRF2 .................................................................................. 4-79
Class CFactorGraph ........................................................................ 4-82
Class CJunctionTree........................................................................ 4-88
Class CDynamicGraphicalModel ..................................................... 4-96
Class CDBN..................................................................................... 4-98
Distribution Functions......................................................................... 4-100
Class CDistribFun.......................................................................... 4-100
Class CTabularDistribFun .............................................................. 4-119
Class CGaussianDistribFun........................................................... 4-125
Class CCondGaussianDistribFun .................................................. 4-132
Class ScalarDistribFun .................................................................. 4-138
Class CTreeDistribFun................................................................... 4-140
Factors................................................................................................ 4-142
Class CFactor ................................................................................ 4-143
Class CCPD................................................................................... 4-159
Class CTabularCPD....................................................................... 4-161
Class CGaussianCPD ................................................................... 4-163
Class CMixtureGaussianCPD........................................................ 4-167
Class CTreeCPD............................................................................ 4-170
Class CPotential ............................................................................ 4-171
Probabilistic Network Library Contents
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Class CTabularPotential................................................................. 4-178
Class CGaussianPotential ............................................................. 4-179
Class CFactors .............................................................................. 4-183
Class CMatrix ..................................................................................... 4-186
Class CDenseMatrix ...................................................................... 4-200
Class CSparseMatrix ..................................................................... 4-204
Class CNumericDenseMatrix......................................................... 4-206
Class CNumericSparseMatrix........................................................ 4-207
Class C2DNumericDenseMatrix.................................................... 4-207
Reference Counter ............................................................................. 4-215
Class CReferenceCounter............................................................. 4-215
Inference Engines............................................................................... 4-217
Class CInfEngine ........................................................................... 4-217
Class CNaiveInfEngine.................................................................. 4-223
Class CPearlInfEngine................................................................... 4-224
Class CSpecPearlInference........................................................... 4-227
Class CJtreeInfEngine ................................................................... 4-230
Class CExInfEngine....................................................................... 4-237
Class CFGSumMaxInfEngine........................................................ 4-239
Class CSamplingInfEngine ............................................................ 4-241
Class CGibbsSamplingInfEngine................................................... 4-245
Class CGibbsWithAnnealingInfEngine........................................... 4-247
Class CLWSamplingInfEngine....................................................... 4-250
Class CDynamicInfEngine ............................................................. 4-254
Class C2TBNInfEngine.................................................................. 4-261
Class C1_ 5SliceInfEngine ............................................................ 4-264
Class C1_5SliceJTreeInfEngine..................................................... 4-265
Class CBKInfEngine ...................................................................... 4-267
Class C2TPFInfEngine .................................................................. 4-269
Learning Engines................................................................................ 4-274
Class CLearningEngine ................................................................. 4-274
Class CStaticLearningEngine ........................................................ 4-276
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