Ebook > Științe > Math > R.G. Buschman & Hari M. Srivastava: Theory and Applications of Convolution Integral Equations (PDF)

R.G. Buschman & Hari M. Srivastava
Theory and Applications of Convolution Integral Equations

Ajutor

Copertina de R.G. Buschman & Hari M. Srivastava: Theory and Applications of Convolution Integral Equations (PDF)

€162.71

Limba Engleză ● Format PDF ● ISBN 9789401580922 ● Editura Springer Netherlands ● Publicat 2013 ● Descărcabil 3 ori ● Valută EUR ● ID 4718832 ● Protecție împotriva copiilor Adobe DRM

Necesită un cititor de ebook capabil de DRM

Mai multe cărți electronice de la același autor (i) / Editor

R.G. Buschman Hari M. Srivastava R. G. Buschman

47.429 Ebooks din această categorie

Copertina de 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 …

Copertina de COMAP: For All Practical Purposes

COMAP: For All Practical Purposes

Copertina de 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 …

Copertina de 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 …

Copertina de 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 …

Copertina de 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 …

Copertina de 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 …

Copertina de 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 …

Copertina de 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 …

Copertina de 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 …

Pagina următoare >>>