Stochastic Complexity In Statistical Inquiry


Book Description

This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.




Stochastic Complexity in Statistical Inquiry


Book Description







Maximum-Entropy and Bayesian Methods in Science and Engineering


Book Description

This volume has its origin in the Fifth, Sixth and Seventh Workshops on and Bayesian Methods in Applied Statistics", held at "Maximum-Entropy the University of Wyoming, August 5-8, 1985, and at Seattle University, August 5-8, 1986, and August 4-7, 1987. It was anticipated that the proceedings of these workshops would be combined, so most of the papers were not collected until after the seventh workshop. Because all of the papers in this volume are on foundations, it is believed that the con tents of this volume will be of lasting interest to the Bayesian community. The workshop was organized to bring together researchers from different fields to critically examine maximum-entropy and Bayesian methods in science and engineering as well as other disciplines. Some of the papers were chosen specifically to kindle interest in new areas that may offer new tools or insight to the reader or to stimulate work on pressing problems that appear to be ideally suited to the maximum-entropy or Bayesian method. A few papers presented at the workshops are not included in these proceedings, but a number of additional papers not presented at the workshop are included. In particular, we are delighted to make available Professor E. T. Jaynes' unpublished Stanford University Microwave Laboratory Report No. 421 "How Does the Brain Do Plausible Reasoning?" (dated August 1957). This is a beautiful, detailed tutorial on the Cox-Polya-Jaynes approach to Bayesian probability theory and the maximum-entropy principle.




Algorithmic Learning Theory


Book Description

This book constitutes the refereed proceedings of the 10th International Conference on Algorithmic Learning Theory, ALT'99, held in Tokyo, Japan, in December 1999. The 26 full papers presented were carefully reviewed and selected from a total of 51 submissions. Also included are three invited papers. The papers are organized in sections on Learning Dimension, Inductive Inference, Inductive Logic Programming, PAC Learning, Mathematical Tools for Learning, Learning Recursive Functions, Query Learning and On-Line Learning.




Model Based Inference in the Life Sciences


Book Description

This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.




Feedforward Neural Network Methodology


Book Description

This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.




Computational Intelligence: Research Frontiers


Book Description

This state-of-the-art survey offers a renewed and refreshing focus on the progress in nature-inspired and linguistically motivated computation. The book presents the expertise and experiences of leading researchers spanning a diverse spectrum of computational intelligence in the areas of neurocomputing, fuzzy systems, evolutionary computation, and adjacent areas. The result is a balanced contribution to the field of computational intelligence that should serve the community not only as a survey and a reference, but also as an inspiration for the future advancement of the state of the art of the field. The 18 selected chapters originate from lectures and presentations given at the 5th IEEE World Congress on Computational Intelligence, WCCI 2008, held in Hong Kong, China, in June 2008. After an introduction to the field and an overview of the volume, the chapters are divided into four topical sections on machine learning and brain computer interface, fuzzy modeling and control, computational evolution, and applications.




Data Science, Classification, and Related Methods


Book Description

This volume contains selected papers covering a wide range of topics, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery. The result is a broad view of the state of the art, making this an essential work not only for data analysts, mathematicians, and statisticians, but also for researchers involved in data processing at all stages from data gathering to decision making.




Machine Learning: ECML'97


Book Description

This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.