Selecting Models from Data

Table of Contents

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   Book Overview
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   Chapter 1 Statistical strategy: step 1
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   Chapter 2 Rational Learning: Finding a Balance Between Utility and Efficiency
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   Chapter 3 A new criterion for selecting models from partially observed data
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   Chapter 4 Small-sample and large-sample statistical model selection criteria
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   Chapter 5 On the choice of penalty term in generalized FPE criterion
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   Chapter 6 Cross-Validation, Stacking and Bi-Level Stacking: Meta-Methods for Classification Learning
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   Chapter 7 Probabilistic approach to model selection: comparison with unstructured data set
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   Chapter 8 Detecting and Explaining Dependencies in Execution Traces
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    Chapter 9 A method for the dynamic selection of models under time constraints
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    Chapter 10 Strategies for Graphical Model Selection
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    Chapter 11 Conditional dependence in probabilistic networks
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    Chapter 12 Reuse and sharing of graphical belief network components
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    Chapter 13 Bayesian Graphical Models for Predicting Errors in Databases
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    Chapter 14 Model Selection for Diagnosis and Treatment Using Temporal Influence Diagrams
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    Chapter 15 Diagnostic systems by model selection: a case study
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    Chapter 16 A Survey of Sampling Methods for Inference on Directed Graphs
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    Chapter 17 Minimizing decision table sizes in influence diagrams: dimension shrinking
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    Chapter 18 Models from Data for Various Types of Reasoning
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    Chapter 19 Causal inference in artificial intelligence
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    Chapter 20 Inferring causal structure among unmeasured variables
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    Chapter 21 When can association graphs admit a causal interpretation?
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    Chapter 22 Inference, Intervention, and Prediction
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    Chapter 23 Attitude Formation Models: Insights from TETRAD
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    Chapter 24 Discovering Probabilistic Causal Relationships: A Comparison Between Two Methods
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    Chapter 25 Path Analysis Models of an Autonomous Agent in a Complex Environment
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    Chapter 26 A Parallel Constructor of Markov Networks
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    Chapter 27 Capturing observations in a nonstationary hidden Markov model
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    Chapter 28 Extrapolating Definite Integral Information
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    Chapter 29 The Software Reliability Consultant
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    Chapter 30 Statistical Reasoning to Enhance User Modelling in Consulting Systems
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    Chapter 31 Selecting a frailty model for longitudinal breast cancer data
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    Chapter 32 Optimal design of reflective sensors using probabilistic analysis
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    Chapter 33 Learning to Catch: Applying Nearest Neighbor Algorithms to Dynamic Control Tasks
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    Chapter 34 Dynamic Recursive Model Class Selection for Classifier Construction
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    Chapter 35 Minimizing the expected costs of classifying patterns by sequential costly inspections
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    Chapter 36 Combining a knowledge-based system and a clustering method for a construction of models in ill-structured domains
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    Chapter 37 Clustering of Symbolically Described Events for Prediction of Numeric Attributes
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    Chapter 38 Symbolic Classifiers: Conditions to Have Good Accuracy Performance
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    Chapter 39 Statistical and neural network techniques for nonparametric regression
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    Chapter 40 Multicollinearity: A tale of two nonparametric regressions
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    Chapter 41 Choice of Order in Regression Strategy
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    Chapter 42 Modelling response models in software
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    Chapter 43 Principal components and model selection
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    Chapter 44 Algorithmic speedups in growing classification trees by using an additive split criterion
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    Chapter 45 Markov Chain Monte Carlo Methods for Hierarchical Bayesian Expert Systems
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    Chapter 46 Simulated annealing in the construction of near-optimal decision trees
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    Chapter 47 SAIGA: Survival of the Fittest in Alaska
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    Chapter 48 A Tool for Model Generation and Knowledge Acquisition
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    Chapter 49 Using knowledge-assisted discriminant analysis to generate new comparative terms