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Books in Mathematics

The Mathematics collection presents a range of foundational and advanced research content across applied and discrete mathematics, including fields such as Computational Mathematics; Differential Equations; Linear Algebra; Modelling & Simulation; Numerical Analysis; Probability & Statistics.

  • Basic Statistics with R

    Reaching Decisions with Data
    • 1st Edition
    • Stephen C. Loftus
    • English
    Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area. In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines.

  • Data Science: Theory and Applications

    • 1st Edition
    • Volume 44
    • English
    Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more. Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods.

  • Geometric Partial Differential Equations - Part 2

    • 1st Edition
    • Volume 22
    • Andrea Bonito + 1 more
    • English
    Besides their intrinsic mathematical interest, geometric partial differential equations (PDEs) are ubiquitous in many scientific, engineering and industrial applications. They represent an intellectual challenge and have received a great deal of attention recently. The purpose of this volume is to provide a missing reference consisting of self-contained and comprehensive presentations. It includes basic ideas, analysis and applications of state-of-the-art fundamental algorithms for the approximation of geometric PDEs together with their impacts in a variety of fields within mathematics, science, and engineering.

  • An Introduction to Hypergeometric, Supertrigonometric, and Superhyperbolic Functions

    • 1st Edition
    • Xiao-Jun Yang
    • English
    An Introduction to Hypergeometric, Supertigonometric, and Superhyperbolic Functions gives a basic introduction to the newly established hypergeometric, supertrigonometric, and superhyperbolic functions from the special functions viewpoint. The special functions, such as the Euler Gamma function, the Euler Beta function, the Clausen hypergeometric series, and the Gauss hypergeometric have been successfully applied to describe the real-world phenomena that involve complex behaviors arising in mathematics, physics, chemistry, and engineering.

  • Reliability Analysis and Plans for Successive Testing

    Start-up Demonstration Tests and Applications
    • 1st Edition
    • Narayanaswamy Balakrishnan + 2 more
    • English
    Reliability Analysis and Plans for Successive Testing: Start-up Demonstration Tests and Applications discusses all past and recent developments on start-up demonstration tests in the context of current numerical and illustrative examples to clarify available methods for distribution theorists and applied mathematicians dealing with control problems. Throughout the book, the authors focus on the panorama of open problems and issues of further interest. As contemporary manufacturers face tremendous commercial pressures to assemble works of high reliability, defined as ‘the probability of the product performing its role under the stated conditions and over a specified period of time’, this book helps address testing issues.

  • AI and Cloud Computing

    • 1st Edition
    • Volume 120
    • English
    AI and Cloud Computing, Volume 120 in the Advances in Computers series, highlights new advances in the field, with this updated volume presenting interesting chapters on topics including A Deep-forest based Approach for Detecting Fraudulent Online Transaction, Design of Cyber-Physical-Socia... Systems with Forensic-awareness Based on Deep Learning, Review on Privacy-preserving Data Comparison Protocols in Cloud Computing, Fingerprint Liveness Detection Using an Improved CNN with the Spatial Pyramid Pooling Structure, Protecting Personal Sensitive Data Security in the Cloud with Blockchain, and more.

  • The Handbook of Reliability, Maintenance, and System Safety through Mathematical Modeling

    • 1st Edition
    • Amit Kumar + 1 more
    • English
    The Handbook of Reliability, Maintenance, and System Safety through Mathematical Modeling discusses the many factors affect reliability and performance, including engineering design, materials, manufacturing, operations, maintenance, and many more. Reliability is one of the fundamental criteria in engineering systems design, with maintenance serving as a way to support reliability throughout a system’s life. Addressing these issues requires information, modeling, analysis and testing. Different techniques are proposed and implemented to help readers analyze various behavior measures (in terms of the functioning and performance) of systems.

  • Mathematical Methods of Analytical Mechanics

    • 1st Edition
    • Henri Gouin
    • English
    Mathematical Methods of Analytical Mechanics uses tensor geometry and geometry of variation calculation, includes the properties associated with Noether's theorem, and highlights methods of integration, including Jacobi's method, which is deduced. In addition, the book covers the Maupertuis principle that looks at the conservation of energy of material systems and how it leads to quantum mechanics. Finally, the book deduces the various spaces underlying the analytical mechanics which lead to the Poisson algebra and the symplectic geometry.

  • Advances in Mathematics for Industry 4.0

    • 1st Edition
    • Mangey Ram
    • English
    Advances in Mathematics for Industry 4.0 examines key tools, techniques, strategies, and methods in engineering applications. By covering the latest knowledge in technology for engineering design and manufacture, chapters provide systematic and comprehensive coverage of key drivers in rapid economic development. Written by leading industry experts, chapter authors explore managing big data in processing information and helping in decision-making, including mathematical and optimization techniques for dealing with large amounts of data in short periods.

  • Introduction to Probability and Statistics for Engineers and Scientists

    • 6th Edition
    • Sheldon M. Ross
    • English
    Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures commonly used by practicing engineers and scientists. Utilizing real data from actual studies across life science, engineering, computing and business, this useful introduction supports reader comprehension through a wide variety of exercises and examples. End-of-chapter reviews of materials highlight key ideas, also discussing the risks associated with the practical application of each material. In the new edition, coverage includes information on Big Data and the use of R. This book is intended for upper level undergraduate and graduate students taking a probability and statistics course in engineering programs as well as those across the biological, physical and computer science departments. It is also appropriate for scientists, engineers and other professionals seeking a reference of foundational content and application to these fields.

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