Information, Statistics and Induction in Science

The Table of Contents for the book is as follows:

• Tutorials

• Invited Presentations

  • First-Order Induction: Techniques and Applications

  • A Universal Regression Model

  • Discovering Theories of Discovering

  • Bayesian Inference and Inductive Inference

  • Does Algorithmic Probability Solve the Problem of Induction?

• Classification Trees, Graphs and Rules

  • Use of Randomization to Normalize Feature Merits

  • A Heuristic Covering Algorithm has Higher Predictive Accuracy Than Learning All Rules

  • Single Pass Constructive Induction with Continuous Variables

  • An MDL Estimate of the Significance of Rules

  • Producing More Comprehensible Models While Retaining Their Performance

  • Constructive Induction of Cartesian Product Attributes

  • The MML Evolution of Classification Graphs

• Regression

  • Estimation of Regression Disturbances Based on Minimum Message Length

  • Minimum Information Estimation of Linear Regression Models

• Foundations of Statistics

  • The Evaluation of Model Selection Criteria: Pointwise Limits in the Parameter Space

  • Fuzzy Hypothesis Tests and Confidence Intervals

  • Maximum Expected Utility Principle: The Case Study of Information Retrieval

  • Multi-Layer Thinking in Logic and Probability

• Biology

  • Fuzzy Gating and Its Application in Flow Cytometry

  • Induction in Medical Discovery: A Computational Simulation

  • Comparative Analysis of Amino Acid Sequences of Proteins Using Rough Sets and Change of Representation

  • Minimum Complexity Principle and its Application to Reconstruction of Molecular Phylogenetic Tree

• Economics

  • Measuring Randomness by Rissanen's Stochastic Complexity: Applications to the Financial Data

  • Conceptual Difficulties with the Efficient Market Hypothesis: Towards a Naturalized Economics

• Philosophy of AI

  • Chance Lowering Causes: Old Problems for New Versions of the Probabilistic Theory of Causation

  • Computer Based Life, Possibilities and Impossibilities

  • Symbolicism and Connectionism: AI Back at a Join Point

• Scientific Discovery

  • Inductive Theories from Equational Systems

  • From Statistical Regularities to Concepts, Hierarchies, Equation Clusters and Rules

• Minimum Encoding Inference

  • Ideal MDL and Its Relation to Bayesianism

  • The Likelihood Principle and MML Estimators

  • False Oracles and Strict MML Estimators

  • An Analysis of SMML from a Subjective Bayesian Perspective

• Mixture Modeling

  • Estimating the Number of Components in a Normal Mixture

  • Comparison of Unsupervised Classifiers

  • An Algorithm for Unsupervised Learning via Normal Mixture Models

  • Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures

  • Mixture Model Clustering of Data Sets with Categorical and Continuous Variables

  • A Minimum Message Length (MML) Model for Software Measures

• Author Index