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Bayesian Designs for Phase I-II Clinical Trials

Bayesian Designs for Phase I-II Clinical Trials

Reliably optimizing a new treatment in humans is a critical first step in clinical evaluation since choosing a suboptimal dose or schedule may lead to failure in later trials. At the same time if promising preclinical results do not translate into a real treatment advance it is important to determine this quickly and terminate the clinical evaluation process to avoid wasting resources. Bayesian Designs for Phase I–II Clinical Trials describes how phase I–II designs can serve as a bridge or protective barrier between preclinical studies and large confirmatory clinical trials. It illustrates many of the severe drawbacks with conventional methods used for early-phase clinical trials and presents numerous Bayesian designs for human clinical trials of new experimental treatment regimes. Written by research leaders from the University of Texas MD Anderson Cancer Center this book shows how Bayesian designs for early-phase clinical trials can explore refine and optimize new experimental treatments. It emphasizes the importance of basing decisions on both efficacy and toxicity. | Bayesian Designs for Phase I-II Clinical Trials

GBP 38.99
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Basic Analysis I Functions of a Real Variable

Basic Analysis I Functions of a Real Variable

Basic Analysis I: Functions of a Real Variable is designed for students who have completed the usual calculus and ordinary differential equation sequence and a basic course in linear algebra. This is a critical course in the use of abstraction but is just first volume in a sequence of courses which prepare students to become practicing scientists. This book is written with the aim of balancing the theory and abstraction with clear explanations and arguments so that students who are from a variety of different areas can follow this text and use it profitably for self-study. It can also be used as a supplementary text for anyone whose work requires that they begin to assimilate more abstract mathematical concepts as part of their professional growth. Features Can be used as a traditional textbook as well as for self-study Suitable for undergraduate mathematics students or for those in other disciplines requiring a solid grounding in abstraction Emphasises learning how to understand the consequences of assumptions using a variety of tools to provide the proofs of propositions | Basic Analysis I Functions of a Real Variable

GBP 82.99
1

Handbook of Alternative Data in Finance Volume I

Handbook of Alternative Data in Finance Volume I

Handbook of Alternative Data in Finance Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data including its definition characteristics difference from conventional data categories of Alternative Data Alternative Data providers and more. The book also offers a rigorous and detailed exploration of process application and delivery that should be practically useful to researchers and practitioners alike. Features Includes cutting edge applications in machine learning fintech and more Suitable for professional quantitative analysts and as a resource for postgraduates and researchers in financial mathematics Features chapters from many leading researchers and practitioners

GBP 140.00
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Performance Reliability and Availability Evaluation of Computational Systems Volume I Performance and Background

Performance Reliability and Availability Evaluation of Computational Systems Volume I Performance and Background

This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance reliability and availability evaluation subjects. The volumes focus on computing systems although the methods may also be applied to other systems. The first volume covers Chapter 1 to Chapter 14 whose subtitle is ``Performance Modeling and Background. The second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling Measuring and Workload and Lifetime Data Analysis. This text is helpful for computer performance professionals for supporting planning design configuring and tuning the performance reliability and availability of computing systems. Such professionals may use these volumes to get acquainted with specific subjects by looking at the particular chapters. Many examples in the textbook on computing systems will help them understand the concepts covered in each chapter. The text may also be helpful for the instructor who teaches performance reliability and availability evaluation subjects. Many possible threads could be configured according to the interest of the audience and the duration of the course. Chapter 1 presents a good number of possible courses programs that could be organized using this text. Volume I is composed of the first two parts besides Chapter 1. Part I gives the knowledge required for the subsequent parts of the text. This part includes six chapters. It covers an introduction to probability descriptive statistics and exploratory data analysis random variables moments covariance some helpful discrete and continuous random variables Taylor series inference methods distribution fitting regression interpolation data scaling distance measures and some clustering methods. Part II presents methods for performance evaluation modeling such as operational analysis Discrete-Time Markov Chains (DTMC) and Continuous Time Markov Chains (CTMC) Markovian queues Stochastic Petri nets (SPN) and discrete event simulation. | Performance Reliability and Availability Evaluation of Computational Systems Volume I Performance and Background

GBP 120.00
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Foundations of Quantitative Finance Book I: Measure Spaces and Measurable Functions

Foundations of Quantitative Finance Book I: Measure Spaces and Measurable Functions

This is the first in a set of 10 books written for professionals in quantitative finance. These books fill the gap between informal mathematical developments found in introductory materials and more advanced treatments that summarize without formally developing the important foundational results professionals need. Book I in the Foundations in Quantitative Finance Series develops topics in measure spaces and measurable functions and lays the foundation for subsequent volumes. Lebesgue and then Borel measure theory are developed on ℝ motivating the general extension theory of measure spaces that follows. This general theory is applied to finite product measure spaces Borel measures on ℝn and infinite dimensional product probability spaces. The overriding goal of these books is a complete and detailed development of the many mathematical theories and results one finds in popular resources in finance and quantitative finance. Each book is dedicated to a specific area of mathematics or probability theory with applications to finance that are relevant to the needs of professionals. Practitioners academic researchers and students will find these books valuable to their career development. All ten volumes are extensively self-referenced. The reader can enter the collection at any point or topic of interest and then work backward to identify and fill in needed details. This approach also works for a course or self-study on a given volume with earlier books used for reference. Advanced quantitative finance books typically develop materials with an eye to comprehensiveness in the given subject matter yet not with an eye toward efficiently curating and developing the theories needed for applications in quantitative finance. This book and series of volumes fill this need. | Foundations of Quantitative Finance Book I: Measure Spaces and Measurable Functions

GBP 68.99
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Beyond First Order Model Theory Volume I and II

Beyond First Order Model Theory Volume I and II

Model theory is the meta-mathematical study of the concept of mathematical truth. After Afred Tarski coined the term Theory of Models in the early 1950’s it rapidly became one of the central most active branches of mathematical logic. In the last few decades ideas that originated within model theory have provided powerful tools to solve problems in a variety of areas of classical mathematics including algebra combinatorics geometry number theory and Banach space theory and operator theory. The two volumes of Beyond First Order Model Theory present the reader with a fairly comprehensive vista rich in width and depth of some of the most active areas of contemporary research in model theory beyond the realm of the classical first-order viewpoint. Each chapter is intended to serve both as an introduction to a current direction in model theory and as a presentation of results that are not available elsewhere. All the articles are written so that they can be studied independently of one another. The first volume is an introduction to current trends in model theory and contains a collection of articles authored by top researchers in the field. It is intended as a reference for students as well as senior researchers. This second volume contains introductions to real-valued logic and applications abstract elementary classes and applications interconnections between model theory and function spaces nonstucture theory and model theory of second-order logic. Features A coherent introduction to current trends in model theory. Contains articles by some of the most influential logicians of the last hundred years. No other publication brings these distinguished authors together. Suitable as a reference for advanced undergraduate postgraduates and researchers. Material presented in the book (e. g abstract elementary classes first-order logics with dependent sorts and applications of infinitary logics in set theory) is not easily accessible in the current literature. The various chapters in the book can be studied independently. | Beyond First Order Model Theory Volume I and II

GBP 230.00
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Sequential Analysis Hypothesis Testing and Changepoint Detection

Sequential Analysis Hypothesis Testing and Changepoint Detection

Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i. i. d. and general non-i. i. d. stochastic models in detail including Markov hidden Markov state-space regression and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms. | Sequential Analysis Hypothesis Testing and Changepoint Detection

GBP 44.99
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Quantum Computation

Quantum Computation

Quantum Computation presents the mathematics of quantum computation. The purpose is to introduce the topic of quantum computing to students in computer science physics and mathematics who have no prior knowledge of this field. The book is written in two parts. The primary mathematical topics required for an initial understanding of quantum computation are dealt with in Part I: sets functions complex numbers and other relevant mathematical structures from linear and abstract algebra. Topics are illustrated with examples focussing on the quantum computational aspects which will follow in more detail in Part II. Part II discusses quantum information quantum measurement and quantum algorithms. These topics provide foundations upon which more advanced topics may be approached with confidence. Features A more accessible approach than most competitor texts which move into advanced research-level topics too quickly for today's students. Part I is comprehensive in providing all necessary mathematical underpinning particularly for those who need more opportunity to develop their mathematical competence. More confident students may move directly to Part II and dip back into Part I as a reference. Ideal for use as an introductory text for courses in quantum computing. Fully worked examples illustrate the application of mathematical techniques. Exercises throughout develop concepts and enhance understanding. End-of-chapter exercises offer more practice in developing a secure foundation.

GBP 74.99
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Computer Systems Architecture

Computer Systems Architecture

Computer Systems Architecture provides IT professionals and students with the necessary understanding of computer hardware. It addresses the ongoing issues related to computer hardware and discusses the solutions supplied by the industry. The book describes trends in computing solutions that led to the current available infrastructures tracing the initial need for computers to recent concepts such as the Internet of Things. It covers computers’ data representation explains how computer architecture and its underlying meaning changed over the years and examines the implementations and performance enhancements of the central processing unit (CPU). It then discusses the organization hierarchy and performance considerations of computer memory as applied by the operating system and illustrates how cache memory significantly improves performance. The author proceeds to explore the bus system algorithms for ensuring data integrity input and output (I/O) components methods for performing I/O various aspects relevant to software engineering and nonvolatile storage devices such as hard drives and technologies for enhancing performance and reliability. He also describes virtualization and cloud computing and the emergence of software-based systems’ architectures. Accessible to software engineers and developers as well as students in IT disciplines this book enhances readers’ understanding of the hardware infrastructure used in software engineering projects. It enables readers to better optimize system usage by focusing on the principles used in hardware systems design and the methods for enhancing performance.

GBP 44.99
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Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R SAS and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features:-Provides an overview of frequentist as well as Bayesian methods. Include a focus on practical aspects and applications. Extensively illustrates the methods with examples using R SAS and BUGS. Full programs are available on a supplementary website. The authors:Kris Bogaerts is project manager at I-BioStat KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials. Arnošt Komárek is associate professor of statistics at Charles University Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society and editor of Statistical Modelling: An International Journal. Emmanuel Lesaffre is professor of biostatistics at I-BioStat KU Leuven. His research interests include Bayesian methods longitudinal data analysis statistical modelling analysis of dental data interval-censored data misclassification issues and clinical trials. He is the founding chair of the Statistical Modelling Society past-president of the International Society for Clinical Biostatistics and fellow of ISI and ASA. | Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

GBP 44.99
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Practical Multivariate Analysis

Practical Multivariate Analysis

This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach with excellent examples and good guidance on computing the book is particularly popular for teaching outside statistics i. e. in epidemiology social science business etc. The sixth edition has been updated with a new chapter on data visualization a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area particularly for non-statisticians. Key Features:Provides a comprehensive practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples guidance on computing and exercises. Updated with a new chapter on data visualization. | Practical Multivariate Analysis

GBP 44.99
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Handbook of Statistics in Clinical Oncology

Handbook of Statistics in Clinical Oncology

Many new challenges have arisen in the area of oncology clinical trials. New cancer therapies are often based on cytostatic or targeted agents which pose new challenges in the design and analysis of all phases of trials. The literature on adaptive trial designs and early stopping has been exploding. Inclusion of high-dimensional data and imaging techniques have become common practice and statistical methods on how to analyse such data have been refined in this area. A compilation of statistical topics relevant to these new advances in cancer research this third edition of Handbook of Statistics in Clinical Oncology focuses on the design and analysis of oncology clinical trials and translational research. Addressing the many challenges that have arisen since the publication of its predecessor this third edition covers the newest developments involved in the design and analysis of cancer clinical trials incorporating updates to all four parts: Phase I trials: Updated recommendations regarding the standard 3 + 3 and continual reassessment approaches along with new chapters on phase 0 trials and phase I trial design for targeted agents. Phase II trials: Updates to current experience in single-arm and randomized phase II trial designs. New chapters include phase II designs with multiple strata and phase II/III designs. Phase III trials: Many new chapters include interim analyses and early stopping considerations phase III trial designs for targeted agents and for testing the ability of markers adaptive trial designs cure rate survival models statistical methods of imaging as well as a thorough review of software for the design and analysis of clinical trials. Exploratory and high-dimensional data analyses: All chapters in this part have been thoroughly updated since the last edition. New chapters address methods for analyzing SNP data and for developing a score based on gene expression data. In addition chapters on risk calculators and forensic bioinformatics have been added. Accessible to statisticians and oncologists interested in clinical trial methodology the book is a single-source collection of up-to-date statistical approaches to research in clinical oncology.

GBP 52.99
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Reproducible Research with R and RStudio

Reproducible Research with R and RStudio

Praise for previous editions:Gandrud has written a great outline of how a fully reproducible research project should look from start to finish with brief explanations of each tool that he uses along the way… Advanced undergraduate students in mathematics statistics and similar fields as well as students just beginning their graduate studies would benefit the most from reading this book. Many more experienced R users or second-year graduate students might find themselves thinking ‘I wish I’d read this book at the start of my studies when I was first learning R!’…This book could be used as the main text for a class on reproducible research … (The American Statistician) Reproducible Research with R and R Studio Third Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. Supplementary materials and example are available on the author’s website. New to the Third Edition Updated package recommendations examples URLs and removed technologies no longer in regular use. More advanced R Markdown (and less LaTeX) in discussions of markup languages and examples. Stronger focus on reproducible working directory tools. Updated discussion of cloud storage services and persistent reproducible material citation. Added discussion of Jupyter notebooks and reproducible practices in industry. Examples of data manipulation with Tidyverse tibbles (in addition to standard data frames) and pivot_longer() and pivot_wider() functions for pivoting data. Features Incorporates the most important advances that have been developed since the editions were published Describes a complete reproducible research workflow from data gathering to the presentation of results Shows how to automatically generate tables and figures using R Includes instructions on formatting a presentation document via markup languages Discusses cloud storage and versioning services particularly Github Explains how to use Unix-like shell programs for working with large research projects | Reproducible Research with R and RStudio

GBP 56.99
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Mathematical Modeling in Biology A Research Methods Approach

Mathematical Modeling in Biology A Research Methods Approach

Mathematical Modeling in Biology: A Research Methods Approach is a textbook written primarily for advanced mathematics and science undergraduate students and graduate-level biology students. Although the applications center on ecology the expertise of the authors the methodology can be imported to any other science including social science and economics. The aim of the book beyond being a useful aid to teaching and learning the core modeling skills needed for mathematical biology is to encourage students to think deeply and clearly about the meaning of mathematics in science and to learn significant research methods. Most importantly it is hoped that students will experience some of the excitement of doing research. Features Minimal pre-requisites beyond a solid background in calculus such as a calculus I course. Suitable for upper division mathematics and sciences students and graduate-level biology students. Provides sample MATLAB codes and instruction in Appendices along with datasets available on https://bit. ly/3fcLF3D | Mathematical Modeling in Biology A Research Methods Approach

GBP 59.99
1

Tree-Based Methods for Statistical Learning in R

Tree-Based Methods for Statistical Learning in R

Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit) and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e. g. Python Spark and Julia) and example usage on real data sets. While the book mostly uses R it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage from the ground up of tree-based methods (e. g. CART conditional inference trees bagging boosting and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package called treemisc which contains several data sets and functions used throughout the book (e. g. there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations) or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining or even improving performance.

GBP 82.99
1

Linear Models with Python

Linear Models with Python

Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. Biometrical Journal Throughout it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. Journal of the Royal Statistical Society Like its widely praised best-selling companion version Linear Models with R this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics from estimation inference and prediction to missing data factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful open source programming language increasingly being used in data science machine learning and computer science. Python and R are similar but R was designed for statistics while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection Shrinkage Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science engineering social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

GBP 82.99
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Promoting Statistical Practice and Collaboration in Developing Countries

Promoting Statistical Practice and Collaboration in Developing Countries

Rarely but just often enough to rebuild hope something happens to confound my pessimism about the recent unprecedented happenings in the world. This book is the most recent instance and I think that all its readers will join me in rejoicing at the good it seeks to do. It is an example of the kind of international comity and collaboration that we could and should undertake to solve various societal problems. This book is a beautiful example of the power of the possible. [It] provides a blueprint for how the LISA 2020 model can be replicated in other fields. Civil engineers or accountants or nurses or any other profession could follow this outline to share expertise and build capacity and promote progress in other countries. It also contains some tutorials for statistical literacy across several fields. The details would change of course but ideas are durable and the generalizations seem pretty straightforward. This book shows every other profession where and how to stand in order to move the world. I urge every researcher to get a copy! —David Banks from the Foreword Promoting Statistical Practice and Collaboration in Developing Countries provides new insights into the current issues and opportunities in international statistics education statistical consulting and collaboration particularly in developing countries around the world. The book addresses the topics discussed in individual chapters from the perspectives of the historical context the present state and future directions of statistical training and practice so that readers may fully understand the challenges and opportunities in the field of statistics and data science especially in developing countries. Features • Reference point on statistical practice in developing countries for researchers scholars students and practitioners • Comprehensive source of state-of-the-art knowledge on creating statistical collaboration laboratories within the field of data science and statistics • Collection of innovative statistical teaching and learning techniques in developing countries Each chapter consists of independent case study contributions on a particular theme that are developed with a common structure and format. The common goal across the chapters is to enhance the exchange of diverse educational and action-oriented information among our intended audiences which include practitioners researchers students and statistics educators in developing countries.

GBP 105.00
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Introductory Analysis An Inquiry Approach

Introductory Analysis An Inquiry Approach

Introductory Analysis: An Inquiry Approach aims to provide a self-contained inquiry-oriented approach to undergraduate-level real analysis. The presentation of the material in the book is intended to be inquiry-oriented' in that as each major topic is discussed details of the proofs are left to the student in a way that encourages an active approach to learning. The book is self-contained in two major ways: it includes scaffolding (i. e. brief guiding prompts marked as Key Steps in the Proof) for many of the theorems. Second it includes preliminary material that introduces students to the fundamental framework of logical reasoning and proof-writing techniques. Students will be able to use the guiding prompts (and refer to the preliminary work) to develop their proof-writing skills. FeaturesStructured in such a way that approximately one week of class can be devoted to each chapterSuitable as a primary text for undergraduates or as a supplementary text for some postgraduate coursesStrikes a unique balance between enquiry-based learning and more traditional approaches to teaching | Introductory Analysis An Inquiry Approach

GBP 44.99
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Geocomputation with R

Geocomputation with R

Geocomputation with R is for people who want to analyze visualize and model geographic data with open source software. It is based on R a statistical programming language that has powerful data processing visualization and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data including those with scientific societal and environmental implications. This book will interest people from many backgrounds especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations aimed at getting you up-to-speed with geographic data in R (II) extensions which covers advanced techniques and (III) applications to real-world problems. The chapters cover progressively more advanced topics with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping) bridges to GIS sharing reproducible code and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems including representing and modeling transport systems finding optimal locations for stores or services and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr. github. io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds where he has taught R for geographic research over many years with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena where he develops and teaches a range of geographic methods with a focus on ecological modeling statistical geocomputing and predictive mapping. All three are active developers and work on a number of R packages including stplanr sabre and RQGIS.

GBP 44.99
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Transition to Advanced Mathematics

Transition to Advanced Mathematics

This unique and contemporary text not only offers an introduction to proofs with a view towards algebra and analysis a standard fare for a transition course but also presents practical skills for upper-level mathematics coursework and exposes undergraduate students to the context and culture of contemporary mathematics. The authors implement the practice recommended by the Committee on the Undergraduate Program in Mathematics (CUPM) curriculum guide that a modern mathematics program should include cognitive goals and offer a broad perspective of the discipline. Part I offers: An introduction to logic and set theory. Proof methods as a vehicle leading to topics useful for analysis topology algebra and probability. Many illustrated examples often drawing on what students already know that minimize conversation about doing proofs. An appendix that provides an annotated rubric with feedback codes for assessing proof writing. Part II presents the context and culture aspects of the transition experience including: 21st century mathematics including the current mathematical culture vocations and careers. History and philosophical issues in mathematics. Approaching reading and learning from journal articles and other primary sources. Mathematical writing and typesetting in LaTeX. Together these Parts provide a complete introduction to modern mathematics both in content and practice. Table of Contents Part I - Introduction to Proofs Logic and Sets Arguments and Proofs Functions Properties of the Integers Counting and Combinatorial Arguments RelationsPart II - Culture History Reading and Writing Mathematical Culture Vocation and Careers History and Philosophy of Mathematics Reading and Researching Mathematics Writing and Presenting Mathematics Appendix A. Rubric for Assessing Proofs Appendix B. Index of Theorems and Definitions from Calculus and Linear Algebra Bibliography Index Biographies Danilo R. Diedrichs is an Associate Professor of Mathematics at Wheaton College in Illinois. Raised and educated in Switzerland he holds a PhD in applied mathematical and computational sciences from the University of Iowa as well as a master’s degree in civil engineering from the Ecole Polytechnique Fédérale in Lausanne Switzerland. His research interests are in dynamical systems modeling applied to biology ecology and epidemiology. Stephen Lovett is a Professor of Mathematics at Wheaton College in Illinois. He holds a PhD in representation theory from Northeastern University. His other books include Abstract Algebra: Structures and Applications (2015) Differential Geometry of Curves and Surfaces with Tom Banchoff (2016) and Differential Geometry of Manifolds (2019). | Transition to Advanced Mathematics

GBP 82.99
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Learn R As a Language

Learn R As a Language

Learning a computer language like R can be either frustrating fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward on overcoming them. This book is designed so that it includes smaller and bigger challenges in what I call playgrounds in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. Although rarely mentioned separately fluency in a computer programming language involves both writing and reading. The parallels between natural and computer languages are many but differences are also important. For students and professionals in the biological sciences humanities and many applied fields recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2020 and beyond. Features R as it is currently used Few prescriptive rules—mostly the author’s preferences together with alternatives Explanation of the R grammar emphasizing the R way of doing things Tutoring for programming in the small using scripts The grammar of graphics and the grammar of data described as grammars Examples of data exchange between R and the foreign world using common file formats Coaching for becoming an independent R user capable of both writing original code and solving future challenges What makes this book different from others: Tries to break the ice and help readers from all disciplines feel at home with R Does not make assumptions about what the reader will use R for Attempts to do only one thing well: guide readers into becoming fluent in the R language Pedro J. Aphalo is a PhD graduate from the University of Edinburgh and is currently a lecturer at the University of Helsinki. A plant biologist and agriculture scientist with a passion for data electronics computers and photography in addition to plants Dr. Aphalo has been a user of R for 25 years. He first organized an R course for MSc students 18 years ago and is the author of 13 R packages currently in CRAN. | Learn R As a Language

GBP 56.99
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Discovering Computer Science Interdisciplinary Problems Principles and Python Programming

Discovering Computer Science Interdisciplinary Problems Principles and Python Programming

Havill's problem-driven approach introduces algorithmic concepts in context and motivates students with a wide range of interests and backgrounds. Janet Davis Associate Professor and Microsoft Chair of Computer Science Whitman College This book looks really great and takes exactly the approach I think should be used for a CS 1 course. I think it really fills a need in the textbook landscape. Marie desJardins Dean of the College of Organizational Computational and Information Sciences Simmons University Discovering Computer Science is a refreshing departure from introductory programming texts offering students a much more sincere introduction to the breadth and complexity of this ever-growing field. James Deverick Senior Lecturer The College of William and Mary This unique introduction to the science of computing guides students through broad and universal approaches to problem solving in a variety of contexts and their ultimate implementation as computer programs. Daniel Kaplan DeWitt Wallace Professor Macalester College Discovering Computer Science: Interdisciplinary Problems Principles and Python Programming is a problem-oriented introduction to computational problem solving and programming in Python appropriate for a first course for computer science majors a more targeted disciplinary computing course or at a slower pace any introductory computer science course for a general audience. Realizing that an organization around language features only resonates with a narrow audience this textbook instead connects programming to students’ prior interests using a range of authentic problems from the natural and social sciences and the digital humanities. The presentation begins with an introduction to the problem-solving process contextualizing programming as an essential component. Then as the book progresses each chapter guides students through solutions to increasingly complex problems using a spiral approach to introduce Python language features. The text also places programming in the context of fundamental computer science principles such as abstraction efficiency testing and algorithmic techniques offering glimpses of topics that are traditionally put off until later courses. This book contains 30 well-developed independent projects that encourage students to explore questions across disciplinary boundaries over 750 homework exercises and 300 integrated reflection questions engage students in problem solving and active reading. The accompanying website — https://www. discoveringcs. net — includes more advanced content solutions to selected exercises sample code and data files and pointers for further exploration. | Discovering Computer Science Interdisciplinary Problems Principles and Python Programming

GBP 74.99
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Solution Techniques for Elementary Partial Differential Equations

Solution Techniques for Elementary Partial Differential Equations

In my opinion this is quite simply the best book of its kind that I have seen thus far. —Professor Peter Schiavone University of Alberta from the Foreword to the Fourth Edition Praise for the previous editions An ideal tool for students taking a first course in PDEs as well as for the lecturers who teach such courses. —Marian Aron Plymouth University UK This is one of the best books on elementary PDEs this reviewer has read so far. Highly recommended. —CHOICE Solution Techniques for Elementary Partial Differential Equations Fourth Edition remains a top choice for a standard undergraduate-level course on partial differential equations (PDEs). It provides a streamlined direct approach to developing students’ competence in solving PDEs and offers concise easily understood explanations and worked examples that enable students to see the techniques in action. New to the Fourth Edition Two additional sections A larger number and variety of worked examples and exercises A companion pdf file containing more detailed worked examples to supplement those in the book which can be used in the classroom and as an aid to online teaching

GBP 56.99
1

Concise Encyclopedia of Coding Theory

Concise Encyclopedia of Coding Theory

Most coding theory experts date the origin of the subject with the 1948 publication of A Mathematical Theory of Communication by Claude Shannon. Since then coding theory has grown into a discipline with many practical applications (antennas networks memories) requiring various mathematical techniques from commutative algebra to semi-definite programming to algebraic geometry. Most topics covered in the Concise Encyclopedia of Coding Theory are presented in short sections at an introductory level and progress from basic to advanced level with definitions examples and many references. The book is divided into three parts: Part I fundamentals: cyclic codes skew cyclic codes quasi-cyclic codes self-dual codes codes and designs codes over rings convolutional codes performance bounds Part II families: AG codes group algebra codes few-weight codes Boolean function codes codes over graphs Part III applications: alternative metrics algorithmic techniques interpolation decoding pseudo-random sequences lattices quantum coding space-time codes network coding distributed storage secret-sharing and code-based-cryptography. Features Suitable for students and researchers in a wide range of mathematical disciplines Contains many examples and references Most topics take the reader to the frontiers of research

GBP 74.99
1