An Introduction to Statistical Learning, James Gareth
Автор: Bishop Название: Pattern Recognition and Machine Learning ISBN: 0387310738 ISBN-13(EAN): 9780387310732 Издательство: Springer Рейтинг: Цена: 6634 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 6540 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.
Автор: Becketti Название: Introduction to Time Series Using Stata ISBN: 1597181323 ISBN-13(EAN): 9781597181327 Издательство: Taylor&Francis Рейтинг: Цена: 7418 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction to Time Series Using Stata provides a step-by-step guide to essential timeseries techniques—from the incredibly simple to the quite complex—and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool.
Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.
Описание: Metaphors, generalizations and unifications are natural and desirable ingredients of the evolution of scientific theories and concepts. This book focuses on nonextensive statistical mechanics, a generalization of Boltzmann-Gibbs (BG) statistical mechanics, one of the greatest monuments of contemporary physics.
Автор: Giraud Название: Introduction to High-Dimensional Statistics ISBN: 1482237946 ISBN-13(EAN): 9781482237948 Издательство: Taylor&Francis Рейтинг: Цена: 5851 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. This highly accessible text describes the challenges related to the analysis of high-dimensional data; covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory; and provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite.
Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.
Автор: Salinas Silvio Название: Introduction to Statistical Physics ISBN: 0387951199 ISBN-13(EAN): 9780387951195 Издательство: Springer Рейтинг: Цена: 8738 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook covers the basic principles of statistical physics and thermodynamics. The text is pitched at the level equivalent to first-year graduate studies or advanced undergraduate studies. After reviewing the basic probability theory of classical thermodynamics, the author addresses the standard topics of statistical physics.
Описание: Monte Carlo Simulation in Statistical Physics deals with the computer simulation of many-body systems in condensed-matter physics and related fields of physics, chemistry and beyond, to traffic flows, stock market fluctuations, etc.). Using random numbers generated by a computer, probability distributions are calculated, allowing the estimation of the thermodynamic properties of various systems. This book describes the theoretical background to several variants of these Monte Carlo methods and gives a systematic presentation from which newcomers can learn to perform such simulations and to analyze their results. This fourth edition has been updated and a new chapter on Monte Carlo simulation of quantum-mechanical problems has been added. To help students in their work a special web server has been installed to host programs and discussion groups (http://wwwcp.tphys.uni-heidelberg.de). Prof. Binder was the winner of the Berni J. Alder CECAM Award for Computational Physics 2001.
Автор: Lomax Название: An Introduction To Statistical Conc ISBN: 0805857397 ISBN-13(EAN): 9780805857399 Издательство: Taylor&Francis Цена: 4175 р. Наличие на складе: Поставка под заказ.
Описание: "Statistical Concepts, 3E" consists of the last 8 chapters of Richard Lomax's best selling text, "An Introduction to Statistical Concepts, 2E". Designed for a
second course in statistics, Lomax's comprehensive and flexible coverage allows instructors to pick and choose those topics most appropriate for their course. It includes topics not
found in competing texts such as the non-parametric and modern alternative procedures and advanced analysis of variance (ANOVA) and regression models.
approach helps students more easily understand the concepts and interpret software results. Throughout the text, the author demonstrates how many statistical concepts relate to one
another. Only the most crucial equations are included.
The new edition features: SPSS sections throughout with input, output, and APA style write-ups using the book's
dataset; a CD with every example and problem dataset used in the text in SPSS format; more information on confidence intervals, effect size measures, power, and regression models; a
revised sequence of the regression and ANOVA chapters for enhanced conceptual flow; de-emphasized computations to provide more discussion of concepts and software; more end
of chapter problems with more realistic data and a greater emphasis on interpretation; many more references; and an Instructor's Resource CD with all of the solutions to the problems
and other teaching aids. "Statistical Concepts, 3E" covers a number of ANOVA and regression models: one-factor; multiple comparison; factorial; ANCOVA; random- and mixed-effect;
hierarchical and randomized blocks; and simple and multiple regression. Realistic examples from education and the behavioural sciences illustrate the concepts.
example includes an examination
of the various procedures and necessary assumptions, tips on developing an APA style write-up, and sample SPSS output. Useful tables of assumptions and the effects of their
violation are included, along with how to test assumptions in SPSS. Each chapter concludes with conceptual and computational problems, about a third of which are new to this
Answers to the odd-numbered problems are provided. It is intended for the second or intermediate course in statistics taught in education and/or behavioural science
departments usually found at the master's or doctoral level and occasionally at the undergraduate level. A prerequisite of descriptive statistics through t-tests is assumed.
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