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Stochastic Complexity in Statistical Inquiry

Stochastic Complexity in Statistical Inquiry PDF Author: Jorma Rissanen
Publisher: World Scientific Publishing Company Incorporated
ISBN: 9789971508593
Category : Business & Economics
Languages : en
Pages : 177

Book Description


Stochastic Complexity in Statistical Inquiry

Stochastic Complexity in Statistical Inquiry PDF Author: Jorma Rissanen
Publisher: World Scientific Publishing Company Incorporated
ISBN: 9789971508593
Category : Business & Economics
Languages : en
Pages : 177

Book Description


Stochastic Complexity In Statistical Inquiry

Stochastic Complexity In Statistical Inquiry PDF Author: Jorma Rissanen
Publisher: World Scientific
ISBN: 9814507407
Category : Technology & Engineering
Languages : en
Pages : 191

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 Theory

Stochastic Complexity in Statistical Inquiry Theory PDF Author: Jorma Rissanen
Publisher: World Scientific Publishing Company Incorporated
ISBN: 9789810203115
Category : Business & Economics
Languages : en
Pages : 188

Book Description


Maximum-Entropy and Bayesian Methods in Science and Engineering

Maximum-Entropy and Bayesian Methods in Science and Engineering PDF Author: G. Erickson
Publisher: Springer Science & Business Media
ISBN: 9400930496
Category : Mathematics
Languages : en
Pages : 321

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.

Stochastic Complexity and Statistics

Stochastic Complexity and Statistics PDF Author: International Business Machines Corporation. Research Division
Publisher:
ISBN:
Category :
Languages : en
Pages : 18

Book Description


Challenges for Computational Intelligence

Challenges for Computational Intelligence PDF Author: Wlodzislaw Duch
Publisher: Springer
ISBN: 3540719849
Category : Technology & Engineering
Languages : en
Pages : 489

Book Description
In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Osamu Watanabe
Publisher: Springer
ISBN: 3540467696
Category : Computers
Languages : en
Pages : 375

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

Model Based Inference in the Life Sciences PDF Author: David R. Anderson
Publisher: Springer Science & Business Media
ISBN: 0387740759
Category : Science
Languages : en
Pages : 203

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

Feedforward Neural Network Methodology PDF Author: Terrence L. Fine
Publisher: Springer Science & Business Media
ISBN: 0387226494
Category : Computers
Languages : en
Pages : 353

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

Computational Intelligence: Research Frontiers PDF Author: Gary G. Yen
Publisher: Springer Science & Business Media
ISBN: 3540688587
Category : Computers
Languages : en
Pages : 402

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.