Ebook: Selecting Models from Data: Artificial Intelligence and Statistics IV

This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour­ aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.

---

This volume presents a selection of papers from the Fourth International Workshop on Artificial Intelligence and Statistics. This biennial workshop brings together researchers from both fields to discuss problems of mutual interest and to compare approaches to their solution. The fourth workshop focused on the topic of selecting models from data. As the papers in this volume attest, the empirical approaches from the two separate fields have much in common yet still depart enough from one another to stimulate active interdisciplinary work. The papers cover a wide spectrum of problems in empirical modelling including model selection in general, graphical models, causal models, regression and other statistical models, and general algorithms and software tools.
This timely volume will benefit all researchers with an active interest in model selection, empirical model building, or more generally the interaction between Statistics and Artificial Intelligence.

---

This volume presents a selection of papers from the Fourth International Workshop on Artificial Intelligence and Statistics. This biennial workshop brings together researchers from both fields to discuss problems of mutual interest and to compare approaches to their solution. The fourth workshop focused on the topic of selecting models from data. As the papers in this volume attest, the empirical approaches from the two separate fields have much in common yet still depart enough from one another to stimulate active interdisciplinary work. The papers cover a wide spectrum of problems in empirical modelling including model selection in general, graphical models, causal models, regression and other statistical models, and general algorithms and software tools.
This timely volume will benefit all researchers with an active interest in model selection, empirical model building, or more generally the interaction between Statistics and Artificial Intelligence.
Content:
Front Matter....Pages i-x
Front Matter....Pages 1-1
Statistical strategy: step 1....Pages 3-9
Rational Learning: Finding a Balance Between Utility and Efficiency....Pages 11-20
A new criterion for selecting models from partially observed data....Pages 21-29
Small-sample and large-sample statistical model selection criteria....Pages 31-39
On the choice of penalty term in generalized FPE criterion....Pages 41-49
Cross-Validation, Stacking and Bi-Level Stacking: Meta-Methods for Classification Learning....Pages 51-59
Probabilistic approach to model selection: comparison with unstructured data set....Pages 61-70
Detecting and Explaining Dependencies in Execution Traces....Pages 71-77
A method for the dynamic selection of models under time constraints....Pages 79-88
Front Matter....Pages 89-89
Strategies for Graphical Model Selection....Pages 91-100
Conditional dependence in probabilistic networks....Pages 101-111
Reuse and sharing of graphical belief network components....Pages 113-122
Bayesian Graphical Models for Predicting Errors in Databases....Pages 123-131
Model Selection for Diagnosis and Treatment Using Temporal Influence Diagrams....Pages 133-142
Diagnostic systems by model selection: a case study....Pages 143-152
A Survey of Sampling Methods for Inference on Directed Graphs....Pages 153-162
Minimizing decision table sizes in influence diagrams: dimension shrinking....Pages 163-172
Models from Data for Various Types of Reasoning....Pages 173-179
Front Matter....Pages 181-181
Causal inference in artificial intelligence....Pages 183-196
Inferring causal structure among unmeasured variables....Pages 197-204
Front Matter....Pages 181-181
When can association graphs admit a causal interpretation?....Pages 205-214
Inference, Intervention, and Prediction....Pages 215-222
Attitude Formation Models: Insights from TETRAD....Pages 223-232
Discovering Probabilistic Causal Relationships: A Comparison Between Two Methods....Pages 233-242
Path Analysis Models of an Autonomous Agent in a Complex Environment....Pages 243-251
Front Matter....Pages 253-253
A Parallel Constructor of Markov Networks....Pages 255-261
Capturing observations in a nonstationary hidden Markov model....Pages 263-271
Extrapolating Definite Integral Information....Pages 273-282
The Software Reliability Consultant....Pages 283-292
Statistical Reasoning to Enhance User Modelling in Consulting Systems....Pages 293-298
Selecting a frailty model for longitudinal breast cancer data....Pages 299-307
Optimal design of reflective sensors using probabilistic analysis....Pages 309-317
Front Matter....Pages 319-319
Learning to Catch: Applying Nearest Neighbor Algorithms to Dynamic Control Tasks....Pages 321-328
Dynamic Recursive Model Class Selection for Classifier Construction....Pages 329-337
Minimizing the expected costs of classifying patterns by sequential costly inspections....Pages 339-349
Combining a knowledge-based system and a clustering method for a construction of models in ill-structured domains....Pages 351-360
Clustering of Symbolically Described Events for Prediction of Numeric Attributes....Pages 361-369
Symbolic Classifiers: Conditions to Have Good Accuracy Performance....Pages 371-380
Front Matter....Pages 381-381
Statistical and neural network techniques for nonparametric regression....Pages 383-392
Multicollinearity: A tale of two nonparametric regressions....Pages 393-402
Front Matter....Pages 381-381
Choice of Order in Regression Strategy....Pages 403-411
Modelling response models in software....Pages 413-424
Principal components and model selection....Pages 425-432
Front Matter....Pages 433-433
Algorithmic speedups in growing classification trees by using an additive split criterion....Pages 435-444
Markov Chain Monte Carlo Methods for Hierarchical Bayesian Expert Systems....Pages 445-452
Simulated annealing in the construction of near-optimal decision trees....Pages 453-462
SAIGA: Survival of the Fittest in Alaska....Pages 463-469
A Tool for Model Generation and Knowledge Acquisition....Pages 471-478
Using knowledge-assisted discriminant analysis to generate new comparative terms....Pages 479-486

---

This volume presents a selection of papers from the Fourth International Workshop on Artificial Intelligence and Statistics. This biennial workshop brings together researchers from both fields to discuss problems of mutual interest and to compare approaches to their solution. The fourth workshop focused on the topic of selecting models from data. As the papers in this volume attest, the empirical approaches from the two separate fields have much in common yet still depart enough from one another to stimulate active interdisciplinary work. The papers cover a wide spectrum of problems in empirical modelling including model selection in general, graphical models, causal models, regression and other statistical models, and general algorithms and software tools.
This timely volume will benefit all researchers with an active interest in model selection, empirical model building, or more generally the interaction between Statistics and Artificial Intelligence.
Content:
Front Matter....Pages i-x
Front Matter....Pages 1-1
Statistical strategy: step 1....Pages 3-9
Rational Learning: Finding a Balance Between Utility and Efficiency....Pages 11-20
A new criterion for selecting models from partially observed data....Pages 21-29
Small-sample and large-sample statistical model selection criteria....Pages 31-39
On the choice of penalty term in generalized FPE criterion....Pages 41-49
Cross-Validation, Stacking and Bi-Level Stacking: Meta-Methods for Classification Learning....Pages 51-59
Probabilistic approach to model selection: comparison with unstructured data set....Pages 61-70
Detecting and Explaining Dependencies in Execution Traces....Pages 71-77
A method for the dynamic selection of models under time constraints....Pages 79-88
Front Matter....Pages 89-89
Strategies for Graphical Model Selection....Pages 91-100
Conditional dependence in probabilistic networks....Pages 101-111
Reuse and sharing of graphical belief network components....Pages 113-122
Bayesian Graphical Models for Predicting Errors in Databases....Pages 123-131
Model Selection for Diagnosis and Treatment Using Temporal Influence Diagrams....Pages 133-142
Diagnostic systems by model selection: a case study....Pages 143-152
A Survey of Sampling Methods for Inference on Directed Graphs....Pages 153-162
Minimizing decision table sizes in influence diagrams: dimension shrinking....Pages 163-172
Models from Data for Various Types of Reasoning....Pages 173-179
Front Matter....Pages 181-181
Causal inference in artificial intelligence....Pages 183-196
Inferring causal structure among unmeasured variables....Pages 197-204
Front Matter....Pages 181-181
When can association graphs admit a causal interpretation?....Pages 205-214
Inference, Intervention, and Prediction....Pages 215-222
Attitude Formation Models: Insights from TETRAD....Pages 223-232
Discovering Probabilistic Causal Relationships: A Comparison Between Two Methods....Pages 233-242
Path Analysis Models of an Autonomous Agent in a Complex Environment....Pages 243-251
Front Matter....Pages 253-253
A Parallel Constructor of Markov Networks....Pages 255-261
Capturing observations in a nonstationary hidden Markov model....Pages 263-271
Extrapolating Definite Integral Information....Pages 273-282
The Software Reliability Consultant....Pages 283-292
Statistical Reasoning to Enhance User Modelling in Consulting Systems....Pages 293-298
Selecting a frailty model for longitudinal breast cancer data....Pages 299-307
Optimal design of reflective sensors using probabilistic analysis....Pages 309-317
Front Matter....Pages 319-319
Learning to Catch: Applying Nearest Neighbor Algorithms to Dynamic Control Tasks....Pages 321-328
Dynamic Recursive Model Class Selection for Classifier Construction....Pages 329-337
Minimizing the expected costs of classifying patterns by sequential costly inspections....Pages 339-349
Combining a knowledge-based system and a clustering method for a construction of models in ill-structured domains....Pages 351-360
Clustering of Symbolically Described Events for Prediction of Numeric Attributes....Pages 361-369
Symbolic Classifiers: Conditions to Have Good Accuracy Performance....Pages 371-380
Front Matter....Pages 381-381
Statistical and neural network techniques for nonparametric regression....Pages 383-392
Multicollinearity: A tale of two nonparametric regressions....Pages 393-402
Front Matter....Pages 381-381
Choice of Order in Regression Strategy....Pages 403-411
Modelling response models in software....Pages 413-424
Principal components and model selection....Pages 425-432
Front Matter....Pages 433-433
Algorithmic speedups in growing classification trees by using an additive split criterion....Pages 435-444
Markov Chain Monte Carlo Methods for Hierarchical Bayesian Expert Systems....Pages 445-452
Simulated annealing in the construction of near-optimal decision trees....Pages 453-462
SAIGA: Survival of the Fittest in Alaska....Pages 463-469
A Tool for Model Generation and Knowledge Acquisition....Pages 471-478
Using knowledge-assisted discriminant analysis to generate new comparative terms....Pages 479-486
....