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Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

This book describes in a basic way the most useful and effective iterative solvers and appropriate preconditioning techniques for some of the most important classes of large and sparse linear systems. The solution of large and sparse linear systems is the most time-consuming part for most of the scientific computing simulations. Indeed mathematical models become more and more accurate by including a greater volume of data but this requires the solution of larger and harder algebraic systems. In recent years research has focused on the efficient solution of large sparse and/or structured systems generated by the discretization of numerical models by using iterative solvers. | Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

GBP 150.00
1

A Course in the Large Sample Theory of Statistical Inference

A Course in the Large Sample Theory of Statistical Inference

This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. Asymptotic results for estimation and testing are derived using the “moving alternative” formulation due to R. A. Fisher and L. Le Cam. Later chapters include discussions of linear rank statistics and of chi-squared tests for contingency table analysis including situations where parameters are estimated from the complete ungrouped data. This book is based on lecture notes prepared by the first author subsequently edited expanded and updated by the second author. Key features: Succinct account of the concept of “asymptotic linearity” and its uses Simplified derivations of the major results under an assumption of joint asymptotic normality Inclusion of numerical illustrations practical examples and advice Highlighting some unexpected consequences of the theory Large number of exercises many with hints to solutions Some facility with linear algebra and with real analysis including ‘epsilon-delta’ arguments is required. Concepts and results from measure theory are explained when used. Familiarity with undergraduate probability and statistics including basic concepts of estimation and hypothesis testing is necessary and experience with applying these concepts to data analysis would be very helpful. | A Course in the Large Sample Theory of Statistical Inference

GBP 84.99
1

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences

From the Foreword:While large-scale machine learning and data mining have greatly impacted a range of commercial applications their use in the field of Earth sciences is still in the early stages. This book edited by AshokSrivastava Ramakrishna Nemani and Karsten Steinhaeuser serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest…I hope that this book will inspire more computer scientists to focus on environmental applications and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences. Vipin Kumar University of MinnesotaLarge-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science computer science statistics and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources in the final chapter of the book.

GBP 44.99
1

Introduction to the Art of Programming Using Scala

Introduction to the Art of Programming Using Scala

With its flexibility for programming both small and large projects Scala is an ideal language for teaching beginning programming. Yet there are no textbooks on Scala currently available for the CS1/CS2 levels. Introduction to the Art of Programming Using Scala presents many concepts from CS1 and CS2 using a modern JVM-based language that works well for both programming in the small and programming in the large. The book progresses from true programming in the small to more significant projects later leveraging the full benefits of object orientation. It first focuses on fundamental problem solving and programming in the small using the REPL and scripting environments. It covers basic logic and problem decomposition and explains how to use GUIs and graphics in programs. The text then illustrates the benefits of object-oriented design and presents a large collection of basic data structures showing different implementations of key ADTs along with more atypical data structures. It also introduces multithreading and networking to provide further motivating examples. By using Scala as the language for both CS1 and CS2 topics this textbook gives students an easy entry into programming small projects as well as a firm foundation for taking on larger-scale projects. Many student and instructor resources are available at www. programmingusingscala. net

GBP 180.00
1

Population Genomics with R

Population Genomics with R

Population Genomics With R presents a multidisciplinary approach to the analysis of population genomics. The methods treated cover a large number of topics from traditional population genetics to large-scale genomics with high-throughput sequencing data. Several dozen R packages are examined and integrated to provide a coherent software environment with a wide range of computational statistical and graphical tools. Small examples are used to illustrate the basics and published data are used as case studies. Readers are expected to have a basic knowledge of biology genetics and statistical inference methods. Graduate students and post-doctorate researchers will find resources to analyze their population genetic and genomic data as well as help them design new studies. The first four chapters review the basics of population genomics data acquisition and the use of R to store and manipulate genomic data. Chapter 5 treats the exploration of genomic data an important issue when analysing large data sets. The other five chapters cover linkage disequilibrium population genomic structure geographical structure past demographic events and natural selection. These chapters include supervised and unsupervised methods admixture analysis an in-depth treatment of multivariate methods and advice on how to handle GIS data. The analysis of natural selection a traditional issue in evolutionary biology has known a revival with modern population genomic data. All chapters include exercises. Supplemental materials are available on-line (http://ape-package. ird. fr/PGR. html).

GBP 48.99
1

Graphical Methods for Data Analysis

Graphical Methods for Data Analysis

This book present graphical methods for analysing data. Some methods are new and some are old some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations a set of data � even a large set- can be adequately analysed through graphical methods alone. In most other situations a few well-chosen graphical displays can significantly enhance numerical statistical analyses. | Graphical Methods for Data Analysis

GBP 205.00
1

Numerical Methods for Unsteady Compressible Flow Problems

Numerical Methods for Unsteady Compressible Flow Problems

Numerical Methods for Unsteady Compressible Flow Problems is written to give both mathematicians and engineers an overview of the state of the art in the field as well as of new developments. The focus is on methods for the compressible Navier-Stokes equations the solutions of which can exhibit shocks boundary layers and turbulence. The idea of the text is to explain the important ideas to the reader while giving enough detail and pointers to literature to facilitate implementation of methods and application of concepts. The book covers high order methods in space such as Discontinuous Galerkin methods and high order methods in time in particular implicit ones. A large part of the text is reserved to discuss iterative methods for the arising large nonlinear and linear equation systems. Ample space is given to both state-of-the-art multigrid and preconditioned Newton-Krylov schemes. Features Applications to aerospace high-speed vehicles heat transfer and more besides Suitable as a textbook for graduate-level courses in CFD or as a reference for practitioners in the field

GBP 44.99
1

Metamodeling for Variable Annuities

Metamodeling for Variable Annuities

This book is devoted to the mathematical methods of metamodeling that can be used to speed up the valuation of large portfolios of variable annuities. It is suitable for advanced undergraduate students graduate students and practitioners. It is the goal of this book to describe the computational problems and present the metamodeling approaches in a way that can be accessible to advanced undergraduate students and practitioners. To that end the book will not only describe the theory of these mathematical approaches but also present the implementations. | Metamodeling for Variable Annuities

GBP 44.99
1

Service-Oriented Distributed Knowledge Discovery

Service-Oriented Distributed Knowledge Discovery

A new approach to distributed large-scale data mining service-oriented knowledge discovery extracts useful knowledge from today’s often unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures. Service-Oriented Distributed Knowledge Discovery presents techniques algorithms and systems based on the service-oriented paradigm. Through detailed descriptions of real software systems it shows how the techniques models and architectures can be implemented. The book covers key areas in data mining and service-oriented computing. It presents the concepts and principles of distributed knowledge discovery and service-oriented data mining. The authors illustrate how to design services for data analytics describe real systems for implementing distributed knowledge discovery applications and explore mobile data mining models. They also discuss the future role of service-oriented knowledge discovery in ubiquitous discovery processes and large-scale data analytics. Highlighting the latest achievements in the field the book gives many examples of the state of the art in service-oriented knowledge discovery. Both novices and more seasoned researchers will learn useful concepts related to distributed data mining and service-oriented data analysis. Developers will also gain insight on how to successfully use service-oriented knowledge discovery in databases (KDD) frameworks.

GBP 59.99
1

Multilevel Modeling Using Mplus

Multilevel Modeling Using Mplus

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts using Mplus as the software tool and demonstrating the various functions available for these analyses in Mplus which is widely used by researchers in various fields including most of the social sciences. In particular Mplus offers users a wide array of tools for latent variable modelling including for multilevel data. | Multilevel Modeling Using Mplus

GBP 180.00
1

The New S Language

The New S Language

This book provides documentation for a new version of the S system released in 1988. The new S enhances the features that have made S popular: interactive computing flexible graphics data management and a large collection of functions. The new S features make possible new applications and higher-level programming including a single unified language user defined functions as first-class objects symbolic computations more accurate numerical calculations and a new approach to graphics. S now provides direct interfaces to the poowerful tool of the UNIX operating system and to algorithms implemented in Fortran and C. | The New S Language

GBP 325.00
1

Randomization Bootstrap and Monte Carlo Methods in Biology

Randomization Bootstrap and Monte Carlo Methods in Biology

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors the fourth edition of Randomization Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization bootstrapping and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap Monte Carlo ANOVA regression and Bayesian methods Makes it easy for biologists researchers and students to understand the methods used Provides information about computer programs and packages to implement calculations particularly using R code Includes a large number of real examples from a range of biological disciplines Written in an accessible style with minimal coverage of theoretical details this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students as well as a reference for researchers from a range of disciplines. The detailed worked examples of real applications will enable practitioners to apply the methods to their own biological data.

GBP 44.99
1

Statistical Modeling and Machine Learning for Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology

Molecular biologists are performing increasingly large and complicated experiments but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs ortholog identification motif finding inference of population structure protein fold prediction and many more. The book takes a pragmatic approach focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics. | Statistical Modeling and Machine Learning for Molecular Biology

GBP 180.00
1

The BUGS Book A Practical Introduction to Bayesian Analysis

The BUGS Book A Practical Introduction to Bayesian Analysis

Bayesian statistical methods have become widely used for data analysis and modelling in recent years and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS including prediction missing data model criticism and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models techniques for criticism and comparison and a wide range of modelling issues before going into the vital area of hierarchical models one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism model comparison sensitivity analysis to alternative priors and thoughtful choice of prior distributions all those aspects of the art of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological the authors systematically work through the large range of tricks that reveal the real power of the BUGS software for example dealing with missing data censoring grouped data prediction ranking parameter constraints and so on. Many of the examples are biostatistical but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples exercises and some solutions can be found on the book‘s website. | The BUGS Book A Practical Introduction to Bayesian Analysis

GBP 180.00
1

Introducing Financial Mathematics Theory Binomial Models and Applications

Introducing Financial Mathematics Theory Binomial Models and Applications

Introducing Financial Mathematics: Theory Binomial Models and Applications seeks to replace existing books with a rigorous stand-alone text that covers fewer examples in greater detail with more proofs. The book uses the fundamental theorem of asset pricing as an introduction to linear algebra and convex analysis. It also provides example computer programs mainly Octave/MATLAB functions but also spreadsheets and Macsyma scripts with which students may experiment on real data. The text's unique coverage is in its contemporary combination of discrete and continuous models to compute implied volatility and fit models to market data. The goal is to bridge the large gaps among nonmathematical finance texts purely theoretical economics texts and specific software-focused engineering texts. | Introducing Financial Mathematics Theory Binomial Models and Applications

GBP 74.99
1

Classification

Classification

As the amount of information recorded and stored electronically grows ever larger it becomes increasingly useful if not essential to develop better and more efficient ways to summarize and extract information from these large multivariate data sets. The field of classification does just that-investigates sets of objects to see if they can be summarized into a small number of classes comprising similar objects. Researchers have made great strides in the field over the last twenty years and classification is no longer perceived as being concerned solely with exploratory analyses. The second edition of Classification incorporates many of the new and powerful methodologies developed since its first edition. Like its predecessor this edition describes both clustering and graphical methods of representing data and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further however by providing critical overviews of recent developments not widely known including efficient clustering algorithms cluster validation consensus classifications and the classification of symbolic data. The author has taken an approach accessible to researchers in the wide variety of disciplines that can benefit from classification analysis and methods. He illustrates the methodologies by applying them to data sets-smaller sets given in the text larger ones available through a Web site. Large multivariate data sets can be difficult to comprehend-the sheer volume and complexity can prove overwhelming. Classification methods provide efficient accurate ways to make them less unwieldy and extract more information. Classification Second Edition offers the ideal vehicle for gaining the background and learning the methodologies-and begin putting these techniques to use.

GBP 59.99
1

Theory of Statistical Inference

Theory of Statistical Inference

Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference and would be suitable for a core course in a graduate program in statistics or biostatistics. The emphasis is on the application of mathematical theory to the problem of inference leading to an optimization theory allowing the choice of those statistical methods yielding the most efficient use of data. The book shows how a small number of key concepts such as sufficiency invariance stochastic ordering decision theory and vector space algebra play a recurring and unifying role. The volume can be divided into four sections. Part I provides a review of the required distribution theory. Part II introduces the problem of statistical inference. This includes the definitions of the exponential family invariant and Bayesian models. Basic concepts of estimation confidence intervals and hypothesis testing are introduced here. Part III constitutes the core of the volume presenting a formal theory of statistical inference. Beginning with decision theory this section then covers uniformly minimum variance unbiased (UMVU) estimation minimum risk equivariant (MRE) estimation and the Neyman-Pearson test. Finally Part IV introduces large sample theory. This section begins with stochastic limit theorems the δ-method the Bahadur representation theorem for sample quantiles large sample U-estimation the Cramér-Rao lower bound and asymptotic efficiency. A separate chapter is then devoted to estimating equation methods. The volume ends with a detailed development of large sample hypothesis testing based on the likelihood ratio test (LRT) Rao score test and the Wald test. Features This volume includes treatment of linear and nonlinear regression models ANOVA models generalized linear models (GLM) and generalized estimating equations (GEE). An introduction to decision theory (including risk admissibility classification Bayes and minimax decision rules) is presented. The importance of this sometimes overlooked topic to statistical methodology is emphasized. The volume emphasizes throughout the important role that can be played by group theory and invariance in statistical inference. Nonparametric (rank-based) methods are derived by the same principles used for parametric models and are therefore presented as solutions to well-defined mathematical problems rather than as robust heuristic alternatives to parametric methods. Each chapter ends with a set of theoretical and applied exercises integrated with the main text. Problems involving R programming are included. Appendices summarize the necessary background in analysis matrix algebra and group theory.

GBP 99.99
1

Partial Differential Equations for Mathematical Physicists

Partial Differential Equations for Mathematical Physicists

Partial Differential Equations for Mathematical Physicists is intended for graduate students researchers of theoretical physics and applied mathematics and professionals who want to take a course in partial differential equations. This book offers the essentials of the subject with the prerequisite being only an elementary knowledge of introductory calculus ordinary differential equations and certain aspects of classical mechanics. We have stressed more the methodologies of partial differential equations and how they can be implemented as tools for extracting their solutions rather than dwelling on the foundational aspects. After covering some basic material the book proceeds to focus mostly on the three main types of second order linear equations namely those belonging to the elliptic hyperbolic and parabolic classes. For such equations a detailed treatment is given of the derivation of Green's functions and of the roles of characteristics and techniques required in handling the solutions with the expected amount of rigor. In this regard we have discussed at length the method of separation variables application of Green's function technique and employment of Fourier and Laplace's transforms. Also collected in the appendices are some useful results from the Dirac delta function Fourier transform and Laplace transform meant to be used as supplementary materials to the text. A good number of problems is worked out and an equally large number of exercises has been appended at the end of each chapter keeping in mind the needs of the students. It is expected that this book will provide a systematic and unitary coverage of the basics of partial differential equations. Key Features An adequate and substantive exposition of the subject. Covers a wide range of important topics. Maintains mathematical rigor throughout. Organizes materials in a self-contained way with each chapter ending with a summary. Contains a large number of worked out problems.

GBP 99.99
1

An Introduction to Numerical Methods A MATLAB Approach

An Introduction to Numerical Methods A MATLAB Approach

An Introduction to Numerical Methods: A MATLAB® Approach Fifth Edition continues to offer readers an accessible and practical introduction to numerical analysis. It presents a wide range of useful and important algorithms for scientific and engineering applications using MATLAB to illustrate each numerical method with full details of the computed results so that the main steps are easily visualized and interpreted. This edition also includes new chapters on Approximation of Continuous Functions and Dealing with Large Sets of Data. Features: Covers the most common numerical methods encountered in science and engineering Illustrates the methods using MATLAB Ideal as an undergraduate textbook for numerical analysis Presents numerous examples and exercises with selected answers provided at the back of the book Accompanied by downloadable MATLAB code hosted at https/www. routledge. com/9781032406824 | An Introduction to Numerical Methods A MATLAB® Approach

GBP 52.99
1

Omic Association Studies with R and Bioconductor

Omic Association Studies with R and Bioconductor

After the great expansion of genome-wide association studies their scientific methodology and notably their data analysis has matured in recent years and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessionsSupplemented by a website including code datasets and solutions | Omic Association Studies with R and Bioconductor

GBP 44.99
1

Natural Language Processing in the Real World Text Processing Analytics and Classification

Natural Language Processing in the Real World Text Processing Analytics and Classification

Natural Language Processing in the Real World is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional academic-taught NLP is often accompanied by a data source or dataset to aid solution building this book is situated in the real world where there may not be an existing rich dataset. This book covers the basic concepts behind NLP and text processing and discusses the applications across 15 industry verticals. From data sources and extraction to transformation and modelling and classic Machine Learning to Deep Learning and Transformers several popular applications of NLP are discussed and implemented. This book provides a hands-on and holistic guide for anyone looking to build NLP solutions from students of Computer Science to those involved in large-scale industrial projects. | Natural Language Processing in the Real World Text Processing Analytics and Classification

GBP 59.99
1

Time Series Clustering and Classification

Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts statisticians mathematicians econometricians computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets including time series data. Time Series Clustering and Classification includes relevant developments on observation-based feature-based and model-based traditional and fuzzy clustering methods feature-based and model-based classification methods and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. FeaturesProvides an overview of the methods and applications of pattern recognition of time seriesCovers a wide range of techniques including unsupervised and supervised approachesIncludes a range of real examples from medicine finance environmental science and moreR and MATLAB code and relevant data sets are available on a supplementary website

GBP 44.99
1

Pattern Discovery in Bioinformatics Theory & Algorithms

Pattern Discovery in Bioinformatics Theory & Algorithms

The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systematic approach to pattern discovery the book supplies sound mathematical definitions and efficient algorithms to explain vital information about biological data. It explores various data patterns including strings clusters permutations topology partial orders and boolean expressions. Each of these classes captures a different form of regularity in the data providing possible answers to a wide range of questions. The book also reviews basic statistics including probability information theory and the central limit theorem. This self-contained book provides a solid foundation in computational methods enabling the solution of difficult biological questions. | Pattern Discovery in Bioinformatics Theory & Algorithms

GBP 59.99
1