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Sets Functions and Logic An Introduction to Abstract Mathematics Third Edition

Sets Functions and Logic An Introduction to Abstract Mathematics Third Edition

Keith Devlin. You know him. You've read his columns in MAA Online you've heard him on the radio and you've seen his popular mathematics books. In between all those activities and his own research he's been hard at work revising Sets Functions and Logic his standard-setting text that has smoothed the road to pure mathematics for legions of undergraduate students. Now in its third edition Devlin has fully reworked the book to reflect a new generation. The narrative is more lively and less textbook-like. Remarks and asides link the topics presented to the real world of students' experience. The chapter on complex numbers and the discussion of formal symbolic logic are gone in favor of more exercises and a new introductory chapter on the nature of mathematics-one that motivates readers and sets the stage for the challenges that lie ahead. Students crossing the bridge from calculus to higher mathematics need and deserve all the help they can get. Sets Functions and Logic Third Edition is an affordable little book that all of your transition-course students not only can afford but will actually read and enjoy and learn from. About the AuthorDr. Keith Devlin is Executive Director of Stanford University's Center for the Study of Language and Information and a Consulting Professor of Mathematics at Stanford. He has written 23 books one interactive book on CD-ROM and over 70 published research articles. He is a Fellow of the American Association for the Advancement of Science a World Economic Forum Fellow and a former member of the Mathematical Sciences Education Board of the National Academy of Sciences . Dr. Devlin is also one of the world's leading popularizers of mathematics. Known as The Math Guy on NPR's Weekend Edition he is a frequent contributor to other local and national radio and TV shows in the US and Britain writes a monthly column for the Web journal MAA Online and regularly writes on mathematics and co | Sets Functions and Logic An Introduction to Abstract Mathematics Third Edition

GBP 175.00
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
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Multiple Imputation in Practice With Examples Using IVEware

Multiple Imputation in Practice With Examples Using IVEware

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis from simple to complex problems using real and simulated data sets. Data sets from cross-sectional retrospective prospective and longitudinal studies randomized clinical trials complex sample surveys are used to illustrate both simple and complex analyses. Version 0. 3 of IVEware the software developed by the University of Michigan is used to illustrate analyses. IVEware can multiply impute missing values analyze multiply imputed data sets incorporate complex sample design features and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows Linux and Mac and with software packages like SAS SPSS Stata and R or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems along with examples of correct analysis techniques. | Multiple Imputation in Practice With Examples Using IVEware

GBP 44.99
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Missing Data Analysis in Practice

Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching teaching and consulting in quantitative areas the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches the underlying assumptions and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts with the data sets and codes available online. The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems such as measurement error finite population inference disclosure limitation combing information from multiple data sources and causal inference.

GBP 44.99
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Pseudolinear Functions and Optimization

Pseudolinear Functions and Optimization

Pseudolinear Functions and Optimization is the first book to focus exclusively on pseudolinear functions a class of generalized convex functions. It discusses the properties characterizations and applications of pseudolinear functions in nonlinear optimization problems. The book describes the characterizations of solution sets of various optimization problems. It examines multiobjective pseudolinear multiobjective fractional pseudolinear static minmax pseudolinear and static minmax fractional pseudolinear optimization problems and their results. The authors extend these results to locally Lipschitz functions using Clarke subdifferentials. They also present optimality and duality results for h-pseudolinear and semi-infinite pseudolinear optimization problems. The authors go on to explore the relationships between vector variational inequalities and vector optimization problems involving pseudolinear functions. They present characterizations of solution sets of pseudolinear optimization problems on Riemannian manifolds as well as results on pseudolinearity of quadratic fractional functions. The book also extends n-pseudolinear functions to pseudolinear and n-pseudolinear fuzzy mappings and characterizations of solution sets of pseudolinear fuzzy optimization problems and n-pseudolinear fuzzy optimization problems. The text concludes with some applications of pseudolinear optimization problems to hospital management and economics. This book encompasses nearly all the published literature on the subject along with new results on semi-infinite nonlinear programming problems. It will be useful to readers from mathematical programming industrial engineering and operations management.

GBP 59.99
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Introductory Statistics for the Health Sciences

Introductory Statistics for the Health Sciences

Introductory Statistics for the Health Sciences takes students on a journey to a wilderness where science explores the unknown providing students with a strong practical foundation in statistics. Using a color format throughout the book contains engaging figures that illustrate real data sets from published research. Examples come from many areas of the health sciences including medicine nursing pharmacy dentistry and physical therapy but are understandable to students in any field. The book can be used in a first-semester course in a health sciences program or in a service course for undergraduate students who plan to enter a health sciences program. The book begins by explaining the research context for statistics in the health sciences which provides students with a framework for understanding why they need statistics as well as a foundation for the remainder of the text. It emphasizes kinds of variables and their relationships throughout giving a substantive context for descriptive statistics graphs probability inferential statistics and interval estimation. The final chapter organizes the statistical procedures in a decision tree and leads students through a process of assessing research scenarios. Web ResourceThe authors have partnered with William Howard Beasley who created the illustrations in the book to offer all of the data sets graphs and graphing code in an online data repository via GitHub. A dedicated website gives information about the data sets and the authors’ electronic flashcards for iOS and Android devices. These flashcards help students learn new terms and concepts.

GBP 44.99
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Analysis of Variance Design and Regression Linear Modeling for Unbalanced Data Second Edition

Analysis of Variance Design and Regression Linear Modeling for Unbalanced Data Second Edition

Analysis of Variance Design and Regression: Linear Modeling for Unbalanced Data Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data. New to the Second EditionReorganized to focus on unbalanced dataReworked balanced analyses using methods for unbalanced dataIntroductions to nonparametric and lasso regressionIntroductions to general additive and generalized additive modelsExamination of homologous factorsUnbalanced split plot analysesExtensions to generalized linear modelsR Minitab® and SAS code on the author’s websiteThe text can be used in a variety of courses including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data. | Analysis of Variance Design and Regression Linear Modeling for Unbalanced Data Second Edition

GBP 44.99
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Applied Bayesian Forecasting and Time Series Analysis

Applied Bayesian Forecasting and Time Series Analysis

Practical in its approach Applied Bayesian Forecasting and Time Series Analysis provides the theories methods and tools necessary for forecasting and the analysis of time series. The authors unify the concepts model forms and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series including how to identify structure explain observed behavior model structures and behaviors and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition fundamental model forms including trends and cycles and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates this unique volume also offers complete guidelines valuable to researchers practitioners and advanced students in statistics operations research and engineering.

GBP 56.99
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Meta-analysis and Combining Information in Genetics and Genomics

Meta-analysis and Combining Information in Genetics and Genomics

Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-Analysis With the diversity of data and meta-data now available there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective. After presenting the basic ideas and tools of meta-analysis the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations. As heterogeneous data sets become more common biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets either of the same type or multiple data sources.

GBP 69.99
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Numerical Methods for Engineers

Numerical Methods for Engineers

Although pseudocodes Mathematica® and MATLAB® illustrate how algorithms work designers of engineering systems write the vast majority of large computer programs in the Fortran language. Using Fortran 95 to solve a range of practical engineering problems Numerical Methods for Engineers Second Edition provides an introduction to numerical methods incorporating theory with concrete computing exercises and programmed examples of the techniques presented. Covering a wide range of numerical applications that have immediate relevancy for engineers the book describes forty-nine programs in Fortran 95. Many of the programs discussed use a sub-program library called nm_lib that holds twenty-three subroutines and functions. In addition there is a precision module that controls the precision of calculations. Well-respected in their field the authors discuss a variety of numerical topics related to engineering. Some of the chapter features include…The numerical solution of sets of linear algebraic equationsRoots of single nonlinear equations and sets of nonlinear equations Numerical quadrature or numerical evaluation of integralsAn introduction to the solution of partial differential equations using finite difference and finite element approachesDescribing concise programs that are constructed using sub-programs wherever possible this book presents many different contexts of numerical analysis forming an excellent introduction to more comprehensive subroutine libraries such as the numerical algorithm group (NAG).

GBP 59.99
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Probability Statistics and Data A Fresh Approach Using R

Probability Statistics and Data A Fresh Approach Using R

This book is a fresh approach to a calculus based first course in probability and statistics using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included using calculus when appropriate but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized quantified using simulations and demonstrated with real data. The book compares parametric and non-parametric methods through simulation allowing for a thorough investigation of testing error and power. The text builds R skills from the outset allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers so that you are working with current real data which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr ggplot2 tidyr stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University and the majority of the more than 400 exercises have been classroom tested. | Probability Statistics and Data A Fresh Approach Using R

GBP 82.99
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Urban Informatics Using Big Data to Understand and Serve Communities

Urban Informatics Using Big Data to Understand and Serve Communities

Urban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management analysis and manipulation using R statistical software. Designed for undergraduate and above level courses this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools. The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them. Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others. Key Features: The technical curriculum consists of both data management and analytics including both as needed to become acquainted with and reveal the content of a new data set. Content that is contextualized in real-world applications relevant to community concerns. Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information. All data sets are publicly available through the Boston Data Portal. | Urban Informatics Using Big Data to Understand and Serve Communities

GBP 48.99
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A Bridge to Higher Mathematics

A Bridge to Higher Mathematics

A Bridge to Higher Mathematics is more than simply another book to aid the transition to advanced mathematics. The authors intend to assist students in developing a deeper understanding of mathematics and mathematical thought. The only way to understand mathematics is by doing mathematics. The reader will learn the language of axioms and theorems and will write convincing and cogent proofs using quantifiers. Students will solve many puzzles and encounter some mysteries and challenging problems. The emphasis is on proof. To progress towards mathematical maturity it is necessary to be trained in two aspects: the ability to read and understand a proof and the ability to write a proof. The journey begins with elements of logic and techniques of proof then with elementary set theory relations and functions. Peano axioms for positive integers and for natural numbers follow in particular mathematical and other forms of induction. Next is the construction of integers including some elementary number theory. The notions of finite and infinite sets cardinality of counting techniques and combinatorics illustrate more techniques of proof. For more advanced readers the text concludes with sets of rational numbers the set of reals and the set of complex numbers. Topics like Zorn‘s lemma and the axiom of choice are included. More challenging problems are marked with a star. All these materials are optional depending on the instructor and the goals of the course.

GBP 175.00
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Introduction to Computational Proteomics

Introduction to Computational Proteomics

Introduction to Computational Proteomics introduces the field of computational biology through a focused approach that tackles the different steps and problems involved with protein analysis classification and meta-organization. The book starts with the analysis of individual entities and works its way through the analysis of more complex entities from protein families to interactions cellular pathways and gene networks. The first part of the book presents methods for identifying the building blocks of the protein space such as motifs and domains. It also describes algorithms for assessing similarity between proteins based on sequence and structure analysis as well as mathematical models such as hidden Markov models and support vector machines that are used to represent protein families and classify new instances. The second part covers methods that investigate higher order structure in the protein space through the application of unsupervised learning algorithms such as clustering and embedding. The book also explores the broader context of proteins. It discusses methods for analyzing gene expression data predicting protein-protein interactions elucidating cellular pathways and reconstructing gene networks. This book provides a coherent and thorough introduction to proteome analysis. It offers rigorous formal descriptions along with detailed algorithmic solutions and models. Each chapter includes problem sets from courses taught by the author at Cornell University and the Technion. Software downloads data sets and other material are available at biozon. org

GBP 59.99
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Bayesian Hierarchical Models With Applications Using R Second Edition

Bayesian Hierarchical Models With Applications Using R Second Edition

An intermediate-level treatment of Bayesian hierarchical models and their applications this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects and one on computing options and strategies. This latter chapter is particularly important due to recent advances in Bayesian computing and estimation including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment while allowing readers to explore alternative likelihood assumptions regression structures and assumptions on prior densities. Features:Provides a comprehensive and accessible overview of applied Bayesian hierarchical modellingIncludes many real data examples to illustrate different modelling topicsR code (based on rjags jagsUI R2OpenBUGS and rstan) is integrated into the book emphasizing implementationSoftware options and coding principles are introduced in new chapter on computingPrograms and data sets available on the book’s website | Bayesian Hierarchical Models With Applications Using R Second Edition

GBP 44.99
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The Elements of Advanced Mathematics

The Elements of Advanced Mathematics

This book has enjoyed considerable use and appreciation during its first four editions. With hundreds of students having learned out of early editions the author continues to find ways to modernize and maintain a unique presentation. What sets the book apart is the excellent writing style exposition and unique and thorough sets of exercises. This edition offers a more instructive preface to assist instructors on developing the course they prefer. The prerequisites are more explicit and provide a roadmap for the course. Sample syllabi are included. As would be expected in a fifth edition the overall content and structure of the book are sound. This new edition offers a more organized treatment of axiomatics. Throughout the book there is a more careful and detailed treatment of the axioms of set theory. The rules of inference are more carefully elucidated. Additional new features include: An emphasis on the art of proof. Enhanced number theory chapter presents some easily accessible but still-unsolved problems. These include the Goldbach conjecture the twin prime conjecture and so forth. The discussion of equivalence relations is revised to present reflexivity symmetry and transitivity before we define equivalence relations. The discussion of the RSA cryptosystem in Chapter 8 is expanded. The author introduces groups much earlier. Coverage of group theory formerly in Chapter 11 has been moved up; this is an incisive example of an axiomatic theory. Recognizing new ideas the author has enhanced the overall presentation to create a fifth edition of this classic and widely-used textbook. | The Elements of Advanced Mathematics

GBP 82.99
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Exploratory Data Analysis with MATLAB

Exploratory Data Analysis with MATLAB

Praise for the Second Edition:The authors present an intuitive and easy-to-read book. … accompanied by many examples proposed exercises good references and comprehensive appendices that initiate the reader unfamiliar with MATLAB. —Adolfo Alvarez Pinto International Statistical Review Practitioners of EDA who use MATLAB will want a copy of this book. … The authors have done a great service by bringing together so many EDA routines but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code pseudo-code and algorithm descriptions to illustrate the concepts. The MATLAB code for examples data sets and the EDA Toolbox are available for download on the book’s website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions such as beanplots and violin plots A chapter on visualizing categorical data

GBP 44.99
1

Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research Fourth Edition describes the analysis of survival data illustrated using a wide range of examples from biomedical research. Written in a non-technical style it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data Cox regression and parametric modelling the book covers many more advanced techniques including interval-censoring frailty modelling competing risks analysis of multiple events and dependent censoring. This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model joint models for longitudinal and survival data and modern methods for the analysis of interval-censored survival data. Features: Presents an accessible account of a wide range of statistical methods for analysing survival data Contains practical guidance on modelling survival data from the author’s many years of experience in teaching and consultancy Shows how Bayesian methods can be used to analyse survival data Includes details on how R can be used to carry out all the methods described with guidance on the interpretation of the resulting output Contains many real data examples and additional data sets that can be used for coursework All data sets used are available in electronic format from the publisher’s website Modelling Survival Data in Medical Research Fourth Edition is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres research scientists and clinicians who are analysing their own data and students following undergraduate or postgraduate courses in survival analysis.

GBP 74.99
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A Course in Categorical Data Analysis

A Course in Categorical Data Analysis

Categorical data-comprising counts of individuals objects or entities in different categories-emerge frequently from many areas of study including medicine sociology geology and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore the ability to manipulate understand and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines. Although t-tests linear regression and analysis of variance are useful valid methods for analysis of measurement data categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students A Course in Categorical Data Analysis presents the easiest most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet readers do not need full knowledge of a statistical software package. In this unique text the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data but on using different models that may lead to meaningful conclusions. The book offers some simple innovative techniques not highighted in other texts that help make the book accessible to a broad interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific medical or real-life conclusions from categorical data sets.

GBP 170.00
1

A Course on Statistics for Finance

A Course on Statistics for Finance

Taking a data-driven approach A Course on Statistics for Finance presents statistical methods for financial investment analysis. The author introduces regression analysis time series analysis and multivariate analysis step by step using models and methods from finance. The book begins with a review of basic statistics including descriptive statistics kinds of variables and types of data sets. It then discusses regression analysis in general terms and in terms of financial investment models such as the capital asset pricing model and the Fama/French model. It also describes mean-variance portfolio analysis and concludes with a focus on time series analysis. Providing the connection between elementary statistics courses and quantitative finance courses this text helps both existing and future quants improve their data analysis skills and better understand the modeling process.

GBP 44.99
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Computational Statistics Handbook with MATLAB

Computational Statistics Handbook with MATLAB

A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB® Third Edition covers today’s most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum emphasizing the implementation of the methods. New to the Third EditionThis third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier support vector machines model checking and regularization partial least squares regression and multivariate adaptive regression splines. Web ResourceThe authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code examples and data sets are available online.

GBP 44.99
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Spectral Theory and Nonlinear Functional Analysis

Spectral Theory and Nonlinear Functional Analysis

This Research Note addresses several pivotal problems in spectral theory and nonlinear functional analysis in connection with the analysis of the structure of the set of zeroes of a general class of nonlinear operators. It features the construction of an optimal algebraic/analytic invariant for calculating the Leray-Schauder degree new methods for solving nonlinear equations in Banach spaces and general properties of components of solutions sets presented with minimal use of topological tools. The author also gives several applications of the abstract theory to reaction diffusion equations and systems. The results presented cover a thirty-year period and include recent unpublished findings of the author and his coworkers. Appealing to a broad audience Spectral Theory and Nonlinear Functional Analysis contains many important contributions to linear algebra linear and nonlinear functional analysis and topology and opens the door for further advances.

GBP 175.00
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
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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
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