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Higher Order Derivatives

The concept of higher order derivatives is useful in many branches of mathematics and its applications. As they are useful in many places nth order derivatives are often defined directly. Higher Order Derivatives discusses these derivatives their uses and the relations among them. It covers higher order generalized derivatives including the Peano d. l. V. P. and Abel derivatives; along with the symmetric and unsymmetric Riemann Cesàro Borel LP- and Laplace derivatives. Although much work has been done on the Peano and de la Vallée Poussin derivatives there is a large amount of work to be done on the other higher order derivatives as their properties remain often virtually unexplored. This book introduces newcomers interested in the field of higher order derivatives to the present state of knowledge. Basic advanced real analysis is the only required background and although the special Denjoy integral has been used knowledge of the Lebesgue integral should suffice.

GBP 59.99

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Rough Multiple Objective Decision Making

Under intense scrutiny for the last few decades Multiple Objective Decision Making (MODM) has been useful for dealing with the multiple-criteria decisions and planning problems associated with many important applications in fields including management science engineering design and transportation. Rough set theory has also proved to be an effective mathematical tool to counter the vague description of objects in fields such as artificial intelligence expert systems civil engineering medical data analysis data mining pattern recognition and decision theory. Rough Multiple Objective Decision Making is perhaps the first book to combine state-of-the-art application of rough set theory rough approximation techniques and MODM. It illustrates traditional techniques—and some that employ simulation-based intelligent algorithms—to solve a wide range of realistic problems. Application of rough theory can remedy two types of uncertainty (randomness and fuzziness) which present significant drawbacks to existing decision-making methods so the authors illustrate the use of rough sets to approximate the feasible set and they explore use of rough intervals to demonstrate relative coefficients and parameters involved in bi-level MODM. The book reviews relevant literature and introduces models for both random and fuzzy rough MODM applying proposed models and algorithms to problem solutions. Given the broad range of uses for decision making the authors offer background and guidance for rough approximation to real-world problems with case studies that focus on engineering applications including construction site layout planning water resource allocation and resource-constrained project scheduling. The text presents a general framework of rough MODM including basic theory models and algorithms as well as a proposed methodological system and discussion of future research.

GBP 74.99

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Object-Orientation Abstraction and Data Structures Using Scala

Praise for the first edition: The well-written comprehensive book [is] aiming to become a de facto reference for the language and its features and capabilities. The pace is appropriate for beginners; programming concepts are introduced progressively through a range of examples and then used as tools for building applications in various domains including sophisticated data structures and algorithms Highly recommended. Students of all levels faculty and professionals/practitioners. D. Papamichail University of Miami in CHOICE Magazine Mark Lewis Introduction to the Art of Programming Using Scala was the first textbook to use Scala for introductory CS courses. Fully revised and expanded the new edition of this popular text has been divided into two books. Object-Orientation Abstraction and Data Structures Using Scala Second Edition is intended to be used as a textbook for a second or third semester course in Computer Science. The Scala programming language provides powerful constructs for expressing both object orientation and abstraction. This book provides students with these tools of object orientation to help them structure solutions to larger more complex problems and to expand on their knowledge of abstraction so that they can make their code more powerful and flexible. The book also illustrates key concepts through the creation of data structures showing how data structures can be written and the strengths and weaknesses of each one. Libraries that provide the functionality needed to do real programming are also explored in the text including GUIs multithreading and networking. The book is filled with end-of-chapter projects and exercises and the authors have also posted a number of different supplements on the book website. Video lectures for each chapter in the book are also available on YouTube. The videos show constr

GBP 180.00

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Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar including probability distributions descriptive and inferential statistical methods and linear modeling. The book assumes knowledge of basic calculus so the presentation can focus on why it works as well as how to do it. Compared to traditional mathematical statistics textbooks however the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e. g. showing an example using the lasso) classification and clustering and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists such as Bayesian inference generalized linear models for non-normal responses (e. g. logistic regression and Poisson loglinear models) and regularized model fitting. The nearly 500 exercises are grouped into Data Analysis and Applications and Methods and Concepts. Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds. rwth-aachen. de/) has expanded R Python and Matlab appendices and all data sets from the examples and exercises. | Foundations of Statistics for Data Scientists With R and Python

GBP 82.99

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Programming for Hybrid Multi/Manycore MPP Systems

Ask not what your compiler can do for you ask what you can do for your compiler. John Levesque Director of Cray’s Supercomputing Centers of ExcellenceThe next decade of computationally intense computing lies with more powerful multi/manycore nodes where processors share a large memory space. These nodes will be the building block for systems that range from a single node workstation up to systems approaching the exaflop regime. The node itself will consist of 10’s to 100’s of MIMD (multiple instruction multiple data) processing units with SIMD (single instruction multiple data) parallel instructions. Since a standard affordable memory architecture will not be able to supply the bandwidth required by these cores new memory organizations will be introduced. These new node architectures will represent a significant challenge to application developers. Programming for Hybrid Multi/Manycore MPP Systems attempts to briefly describe the current state-of-the-art in programming these systems and proposes an approach for developing a performance-portable application that can effectively utilize all of these systems from a single application. The book starts with a strategy for optimizing an application for multi/manycore architectures. It then looks at the three typical architectures covering their advantages and disadvantages. The next section of the book explores the other important component of the target—the compiler. The compiler will ultimately convert the input language to executable code on the target and the book explores how to make the compiler do what we want. The book then talks about gathering runtime statistics from running the application on the important problem sets previously discussed. How best to utilize available memory bandwidth and virtualization is covered next along with hybridization of a program. The last part of the book includes several major applications and examines future hardware advancements and how the application developer may prepare for those advancements.

GBP 44.99

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Building a Platform for Data-Driven Pandemic Prediction From Data Modelling to Visualisation - The CovidLP Project

This book is about building platforms for pandemic prediction. It provides an overview of probabilistic prediction for pandemic modeling based on a data-driven approach. It also provides guidance on building platforms with currently available technology using tools such as R Shiny and interactive plotting programs. The focus is on the integration of statistics and computing tools rather than on an in-depth analysis of all possibilities on each side. Readers can follow different reading paths through the book depending on their needs. The book is meant as a basis for further investigation of statistical modelling implementation tools monitoring aspects and software functionalities. Features: A general but parsimonious class of models to perform statistical prediction for epidemics using a Bayesian approach Implementation of automated routines to obtain daily prediction results How to interactively visualize the model results Strategies for monitoring the performance of the predictions and identifying potential issues in the results Discusses the many decisions required to develop and publish online platforms Supplemented by an R package and its specific functionalities to model epidemic outbreaks The book is geared towards practitioners with an interest in the development and presentation of results in an online platform of statistical analysis of epidemiological data. The primary audience includes applied statisticians biostatisticians computer scientists epidemiologists and professionals interested in learning more about epidemic modelling in general including the COVID-19 pandemic and platform building. The authors are professors at the Statistics Department at Universidade Federal de Minas Gerais. Their research records exhibit contributions applied to a number of areas of Science including Epidemiology. Their research activities include books published with Chapman and Hall/CRC and papers in high quality journals. They have also been involved with academic management of graduate programs in Statistics and one of them is currently the President of the Brazilian Statistical Association. | Building a Platform for Data-Driven Pandemic Prediction From Data Modelling to Visualisation - The CovidLP Project

GBP 56.99

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