Електронна книга > Наука > Математика > Ludwig Arnold & Volker Wihstutz: Lyapunov Exponents (PDF)

Ludwig Arnold & Volker Wihstutz
Lyapunov Exponents
Proceedings of a Workshop held in Bremen, November 12-15, 1984

Підтримка

Обкладинка Ludwig Arnold & Volker Wihstutz: Lyapunov Exponents (PDF)

€33.58

Мова Англійська ● Формат PDF ● ISBN 9783540397953 ● Редактор Ludwig Arnold & Volker Wihstutz ● Видавець Springer Berlin Heidelberg ● Опубліковано 2006 ● Завантажувані 3 разів ● Валюта EUR ● Посвідчення особи 6593188 ● Захист від копіювання Adobe DRM

Потрібен читач електронних книг, що підтримує DRM

Більше електронних книг того самого автора / Редактор

Ludwig Arnold Volker Wihstutz

47 429 Електронні книги в цій категорі

Обкладинка Jiling (Auckland University of Technology, New Zealand) Cao & Warren B. (The University of Auckland, New Zealand) Moors: Separate and Joint Continuity

Jiling (Auckland University of Technology, New Zealand) Cao & Warren B. (The University of Auckland, New Zealand) Moors: Separate and Joint Continuity

Separate and Joint Continuity presents and summarises the main ideas and theorems that have been developed on this topic, which lies at the interface between General Topology and Functional Analysis …

Обкладинка COMAP: For All Practical Purposes

COMAP: For All Practical Purposes

Обкладинка Lucia Darrow & Walter DeGrange: Field Guide to Compelling Analytics

Lucia Darrow & Walter DeGrange: Field Guide to Compelling Analytics

Field Guide to Compelling Analytics is written for Analytics Professionals (APs) who want to increase their probability of success in implementing analytical solutions. In the past, soft skills such …

Обкладинка Michael J. (University of Florida, Gainesville, USA) Daniels & Antonio Linero: Bayesian Nonparametrics for Causal Inference and Missing Data

Michael J. (University of Florida, Gainesville, USA) Daniels & Antonio Linero: Bayesian Nonparametrics for Causal Inference and Missing Data

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional rela …

Обкладинка Chris (University of Bath, UK) Chatfield: Problem Solving

Chris (University of Bath, UK) Chatfield: Problem Solving

This book illuminates the complex process of problem solving, including formulating the problem, collecting and analyzing data, and presenting the …

Обкладинка Rudolf Taschner: Numbers at Work

Rudolf Taschner: Numbers at Work

Drawing primarily from historical examples, this book explains the tremendous role that numbers and, in particular, mathematics play in all aspects of our civilization and culture. The lively style …

Обкладинка Martin Hugh (University of Victoria, Division of Continuing Studies, Canada) Monkman: The Data Preparation Journey

Martin Hugh (University of Victoria, Division of Continuing Studies, Canada) Monkman: The Data Preparation Journey

The Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With t …

Обкладинка Wentao Huang & Jibin Li: Planar Dynamical Systems

Wentao Huang & Jibin Li: Planar Dynamical Systems

In 2008, November 23-28, the workshop of ”Classical Problems on Planar Polynomial Vector Fields ” was held in the Banff International Research Station, Canada. Called ‘classical problems’, it was con …

Обкладинка H.R. Madala: Inductive Learning Algorithms for Complex Systems Modeling

H.R. Madala: Inductive Learning Algorithms for Complex Systems Modeling

Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and …

Обкладинка John E. Kolassa: An Introduction to Nonparametric Statistics

John E. Kolassa: An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data.　Rank-based and resamplin …

Наступна сторінка >>>