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Philosophy of Statistics
Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling "restricted by their disciplines or thinking "piecemeal in their treatment of issues. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers' favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines. - Provides a bridge between philosophy and current scientific findings - Covers theory and applications - Encourages multi-disciplinary dialogue
The Operatives
A history of a 20th Century masonic society dedicated to the preservation of the rites and ceremonies of the antient Guild stonemasons.
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 11th International Conference on Algorithmic Learning Theory, ALT 2000, held in Sydney, Australia in December 2000. The 22 revised full papers presented together with three invited papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.
Telephone Directory
Each issue includes a classified section on the organization of the Dept.
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996. The 16 revised full papers presented were selected from 41 submissions; also included are eight short papers as well as four full length invited contributions by Ross Quinlan, Takeshi Shinohara, Leslie Valiant, and Paul Vitanyi, and an introduction by the volume editors. The book covers all areas related to algorithmic learning theory, ranging from theoretical foundations of machine learning to applications in several areas.
Specification Case Studies in RAISE
This volume presents twelve case studies that use RAISE - Rigorous Approach to Industrial Software Engineering - to construct, analyse, develop and apply formal specifications. The case studies cover a wide range of application areas including government finance, case-based reasoning, multi-language text processing, object-oriented design patterns, component-based software design and natural resource management. By illustrating the variety of uses of formal specifications, the case studies also raise questions about the creation, purpose and scope of formal models before they are built. Additional resources and complete specifications for all of the case studies and the RAISE tools used to process them, are available on the World Wide Web. This book will be of particular interest to software engineers, especially those responsible for the initial stages of requirements engineering and software architecture and design. It will also be of interest to academics and students on advanced formal methods courses.
Telephone Directory
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Information, Statistics And Induction In Science - Proceedings Of The Conference, Isis '96
This conference will explore the use of computational modelling to understand and emulate inductive processes in science. The problems involved in building and using such computer models reflect methodological and foundational concerns common to a variety of academic disciplines, especially statistics, artificial intelligence (AI) and the philosophy of science. This conference aims to bring together researchers from these and related fields to present new computational technologies for supporting or analysing scientific inference and to engage in collegial debate over the merits and difficulties underlying the various approaches to automating inductive and statistical inference.The proceedings also include abstracts by the invited speakers (J R Quinlan, J J Rissanen, M Minsky, R J Solomonoff & H Kyburg, Jr.).
