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Fundamentals
Introduction 3
Expert Systems 3
Representation of Uncertainty 4
Normative Expert Systems 5
Rule-Based Systems 5
Causality 6
Uncertainty in Rule-Based Systems 7
Explaining Away 7
Bayesian Networks 8
Inference in Bayesian Networks 9
Construction of Bayesian Networks 10
An Example 10
Bayesian Decision Problems 13
When to Use Probabilistic Nets 14
Concluding Remarks 15
Networks 17
Graphs 18
Graphical Models 20
Variables 20
Vertices vs. Variables 21
Taxonomy of Vertices/Variables 22
Vertex Symbols 23
Summary of Notation 23
Evidence 23
Causality 24
Flow of Information in Causal Networks 25
Serial Connections 26
Diverging Connections 28
Converging Connections 29
The Importance of Correct Modeling of Causality 30
Two Equivalent IrrelevanceCriteria 31
d-Separation Criterion 32
Directed Global Markov Criterion 33
Summary 35
Probabilities 37
Basics 38
Events 38
Conditional Probability 38
Axioms 39
Probability Distributions for Variables 40
Rule of Total Probability 41
Graphical Representation 43
Probability Potentials 44
Normalization 44
Evidence Potentials 45
Potential Calculus 46
Barren Variables 49
Fundamental Rule and Bayes' Rule 50
Interpretation of Bayes' Rule 51
Bayes' Factor 53
Independence 54
Independence and DAGs 56
Chain Rule 58
Summary 60
Probabilistic Networks 63
Reasoning Under Uncertainty 64
Discrete Bayesian Networks 65
Conditional Linear Gaussian Bayesian Networks 70
Decision Making Under Uncertainty 74
Discrete Influence Diagrams 75
Conditional LQG Influence Diagrams 85
Limited Memory Influence Diagrams 89
Object-Oriented Probabilistic Networks 91
Chain Rule 96
Unfolded OOPNs 96
Instance Trees 97
Inheritance 98
Dynamic Models 98
Summary 102
Solving Probabilistic Networks 107
Probabilistic Inference 108
Inference in Discrete Bayesian Networks 108
Inference in CLG Bayesian Networks 121
Solving Decision Models 124
Solving Discrete Influence Diagrams 124
Solving CLQG Influence Diagrams 129
Relevance Reasoning 130
Solving LIMIDs 133
Solving OOPNs 136
Summary 137
Model Construction
Eliciting the Model 143
When to Use Probabilistic Networks 144
Characteristics of Probabilistic Networks 145
Some Criteria for Using Probabilistic Networks 145
Identifying the Variables of a Model 147
Well-Defined Variables 147
Types of Variables 150
Eliciting the Structure 152
A Basic Approach 152
Idioms 154
Model Verification 159
Eliciting the Numbers 163
Eliciting Subjective Conditional Probabilities 163
Eliciting Subjective Utilities 166
Specifying CPTs and UTs Through Expressions 166
Concluding Remarks 170
Summary 172
Modeling Techniques 177
Structure Related Techniques 177
Parent Divorcing 178
Temporal Transformation 182
Structural and Functional Uncertainty 184
Undirected Dependence Relations 188
Bidirectional Relations 191
Naive Bayes Model 193
Probability Distribution Related Techniques 196
Measurement Uncertainty 196
Expert Opinions 199
Node Absorption 201
Set Value by Intervention 202
Independence of Causal Influence 205
Mixture of Gaussian Distributions 210
Decision Related Techniques 212
Test Decisions 212
Missing Informational Links 216
Missing Observations 218
Hypothesis of Highest Probability 220
Constraints on Decisions 223
Summary 225
Data-Driven Modeling 227
The Task and Basic Assumptions 228
Structure Learning From Data 229
Basic Assumptions 230
Equivalent Models 231
Statistical Hypothesis Tests 232
Structure Constraints 235
PC Algorithm 235
PC* Algorithm 241
NPC Algorithm 241
Batch Parameter Learning From Data 246
Expectation-Maximization Algorithm 247
Penalized EM Algorithm 249
Sequential Parameter Learning 252
Summary 254
Model Analysis
Conflict Analysis 261
Evidence Driven Conflict Analysis 262
Conflict Measure 262
Tracing Conflicts 264
Conflict Resolution 265
Hypothesis Driven Conflict Analysis 267
Cost-of-Omission Measure 267
Evidence with Conflict Impact 267
Summary 269
Sensitivity Analysis 273
Evidence Sensitivity Analysis 274
Distance and Cost-of-Omission Measures 275
Identify Minimum and Maximum Beliefs 276
Impact of Evidence Subsets 277
Discrimination of Competing Hypotheses 278
What-If Analysis 279
Impact of Findings 280
Parameter Sensitivity Analysis 281
Sensitivity Function 282
Sensitivity Value 285
Admissible Deviation 286
Summary 287
Value of Information Analysis 291
VOI Analysis in Bayesian Networks 292
Entropy and Mutual Information 292
Hypothesis Driven Value of Information Analysis 293
VOI Analysis in Influence Diagrams 297
Summary 300
References 305
List of Symbols 311
Index 313
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Add Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence. This book provides a comprehensive guide for practitioners who wish to under, Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis to the inventory that you are selling on WonderClubX
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Add Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence. This book provides a comprehensive guide for practitioners who wish to under, Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis to your collection on WonderClub |