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Handbook of Biomarkers and Precision Medicine

Handbook of Biomarkers and Precision Medicine

The field of Biomarkers and Precision Medicine in drug development is rapidly evolving and this book presents a snapshot of exciting new approaches. By presenting a wide range of biomarker applications discussed by knowledgeable and experienced scientists readers will develop an appreciation of the scope and breadth of biomarker knowledge and find examples that will help them in their own work. Maria Freire Foundation for the National Institutes of HealthHandbook of Biomarkers and Precision Medicine provides comprehensive insights into biomarker discovery and development which has driven the new era of Precision Medicine. A wide variety of renowned experts from government academia teaching hospitals biotechnology and pharmaceutical companies share best practices examples and exciting new developments. The handbook aims to provide in-depth knowledge to research scientists students and decision makers engaged in Biomarker and Precision Medicine-centric drug development. Features:Detailed insights into biomarker discovery validation and diagnostic development with implementation strategiesLessons-learned from successful Precision Medicine case studiesA variety of exciting and emerging biomarker technologiesThe next frontiers and future challenges of biomarkers in Precision MedicineClaudio Carini Mark Fidock and Alain van Gool are internationally recognized as scientific leaders in Biomarkers and Precision Medicine. They have worked for decades in academia and pharmaceutical industry in EU USA and Asia. Currently Dr. Carini is Honorary Faculty at Kings’s College School of Medicine London UK. Dr. Fidock is Vice President of Precision Medicine Laboratories at AstraZeneca Cambridge UK. Prof. dr. van Gool is Head Translational Metabolic Laboratory at Radboud university medical school Nijmegen NL.

GBP 66.99
1

Bayesian Applications in Pharmaceutical Development

Bayesian Applications in Pharmaceutical Development

The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2. 6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development from discovery to clinical trial to manufacturing with practical examples. This book will have a wide appeal to statisticians scientists and physicians working in drug development who are motivated to accelerate and streamline the drug development process as well as students who aspire to work in this field. The advantages of this book are: Provides motivating worked practical case examples with easy to grasp models technical details and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles technical reports and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University Dallas Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process. | Bayesian Applications in Pharmaceutical Development

GBP 44.99
1

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
1

Statistics and Health Care Fraud How to Save Billions

Statistics and Health Care Fraud How to Save Billions

Statistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used in health care fraud audits without requiring any mathematical background. The public suffers from health care overpayments either directly as patients or indirectly as taxpayers and fraud analytics provides ways to handle the large size and complexity of these claims. The book starts with a brief overview of global healthcare systems such as U. S. Medicare. This is followed by a discussion of medical overpayments and assessment initiatives using a variety of real world examples. The book covers subjects as: • Description and visualization of medical claims data • Prediction of fraudulent transactions • Detection of excessive billings • Revealing new fraud patterns • Challenges and opportunities with health care fraud analytics Dr. Tahir Ekin is the Brandon Dee Roberts Associate Professor of Quantitative Methods in McCoy College of Business Texas State University. His previous work experience includes a working as a statistician on health care fraud detection. His scholarly work on health care fraud has been published in a variety of academic journals including International Statistical Review The American Statistician and Applied Stochastic Models in Business and Industry. He is a recipient of the Texas State University 2018 Presidential Distinction Award in Scholar Activities and the ASA/NISS y-Bis 2016 Best Paper Awards. He has developed and taught courses in the areas of business statistics optimization data mining and analytics. Dr. Ekin also serves as Vice President of the International Society for Business and Industrial Statistics. | Statistics and Health Care Fraud How to Save Billions

GBP 24.99
1

Probability Methods for Cost Uncertainty Analysis A Systems Engineering Perspective Second Edition

Probability Methods for Cost Uncertainty Analysis A Systems Engineering Perspective Second Edition

Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective Second Edition gives you a thorough grounding in the analytical methods needed for modeling and measuring uncertainty in the cost of engineering systems. This includes the treatment of correlation between the cost of system elements how to present the analysis to decision-makers and the use of bivariate probability distributions to capture joint interactions between a system’s cost and schedule. Analytical techniques from probability theory are stressed along with the Monte Carlo simulation method. Numerous examples and case discussions illustrate the practical application of theoretical concepts. While the original chapters from the first edition remain unchanged this second edition contains new material focusing on the application of theory to problems encountered in practice. Highlights include the use of GERM to build development and production cost estimating relationships as well as the eSBM which was developed from a need in the community to offer simplified analytical alternatives to advanced probability-based approaches. The book also lists the major technical works of the late Dr. Stephen A. Book a mathematician and world-renowned cost analyst whose contributions advanced the theory and practice of cost risk analysis. | Probability Methods for Cost Uncertainty Analysis A Systems Engineering Perspective Second Edition

GBP 44.99
1

Measuring Society

Measuring Society

Collecting and analyzing data on unemployment inflation and inequality help describe the complex world around us. When published by the government such data are called official statistics. They are reported by the media used by politicians to lend weight to their arguments and by economic commentators to opine about the state of society. Despite such widescale use explanations about how these measures are constructed are seldom provided for a non-technical reader. This Measuring Society book is a short accessible guide to six topics: jobs house prices inequality prices for goods and services poverty and deprivation. Each relates to concepts we use on a personal level to form an understanding of the society in which we live: We need a job a place to live and food to eat. Using data from the United States we answer three basic questions: why how and for whom these statistics have been constructed. We add some context and flavor by discussing the historical background. This book provides the reader with a good grasp of these measures. Chaitra H. Nagaraja is an Associate Professor of Statistics at the Gabelli School of Business at Fordham University in New York. Her research interests include house price indices and inequality measurement. Prior to Fordham Dr. Nagaraja was a researcher at the U. S. Census Bureau. While there she worked on projects relating to the American Community Survey.

GBP 24.99
1

Algebraic Number Theory A Brief Introduction

Algebraic Number Theory A Brief Introduction

This book offers the basics of algebraic number theory for students and others who need an introduction and do not have the time to wade through the voluminous textbooks available. It is suitable for an independent study or as a textbook for a first course on the topic. The author presents the topic here by first offering a brief introduction to number theory and a review of the prerequisite material then presents the basic theory of algebraic numbers. The treatment of the subject is classical but the newer approach discussed at the end provides a broader theory to include the arithmetic of algebraic curves over finite fields and even suggests a theory for studying higher dimensional varieties over finite fields. It leads naturally to the Weil conjecture and some delicate questions in algebraic geometry. About the Author Dr. J. S. Chahal is a professor of mathematics at Brigham Young University. He received his Ph. D. from Johns Hopkins University and after spending a couple of years at the University of Wisconsin as a post doc he joined Brigham Young University as an assistant professor and has been there ever since. He specializes and has published several papers in number theory. For hobbies he likes to travel and hike. His book Fundamentals of Linear Algebra is also published by CRC Press. | Algebraic Number Theory A Brief Introduction

GBP 52.99
1

Fortran 2018 with Parallel Programming

Fortran 2018 with Parallel Programming

The programming language Fortran dates back to 1957 when a team of IBM engineers released the first Fortran Compiler. During the past 60 years the language had been revised and updated several times to incorporate more features to enable writing clean and structured computer programs. The present version is Fortran 2018. Since the dawn of the computer era there had been a constant demand for a “larger” and “faster” machine. To increase the speed there are three hurdles. The density of the active components on a VLSI chip cannot be increased indefinitely and with the increase of the density heat dissipation becomes a major problem. Finally the speed of any signal cannot exceed the velocity of the light. However by using several inexpensive processors in parallel coupled with specialized software and hardware programmers can achieve computing speed similar to a supercomputer. This book can be used to learn the modern Fortran from the beginning and the technique of developing parallel programs using Fortran. It is for anyone who wants to learn Fortran. Knowledge beyond high school mathematics is not required. There is not another book on the market yet which deals with Fortran 2018 as well as parallel programming. FEATURES Descriptions of majority of Fortran 2018 instructions Numerical Model String with Variable Length IEEE Arithmetic and Exceptions Dynamic Memory Management Pointers Bit handling C-Fortran Interoperability Object Oriented Programming Parallel Programming using Coarray Parallel Programming using OpenMP Parallel Programming using Message Passing Interface (MPI) THE AUTHOR Dr Subrata Ray is a retired Professor Indian Association for the Cultivation of Science Kolkata. | Fortran 2018 with Parallel Programming

GBP 140.00
1

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R: Learning with Case Studies Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition this new edition is divided into two parts. The first part will feature introductory material including a new chapter that provides an introduction to data mining to complement the already existing introduction to R. The second part includes case studies and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools as well as researchers and developers the book should be useful for anyone interested in entering the world of R and data mining. About the AuthorLuís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. | Data Mining with R Learning with Case Studies Second Edition

GBP 44.99
1

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

A First Course in Ordinary Differential Equations

A First Course in Ordinary Differential Equations

A First course in Ordinary Differential Equations provides a detailed introduction to the subject focusing on analytical methods to solve ODEs and theoretical aspects of analyzing them when it is difficult/not possible to find their solutions explicitly. This two-fold treatment of the subject is quite handy not only for undergraduate students in mathematics but also for physicists engineers who are interested in understanding how various methods to solve ODEs work. More than 300 end-of-chapter problems with varying difficulty are provided so that the reader can self examine their understanding of the topics covered in the text. Most of the definitions and results used from subjects like real analysis linear algebra are stated clearly in the book. This enables the book to be accessible to physics and engineering students also. Moreover sufficient number of worked out examples are presented to illustrate every new technique introduced in this book. Moreover the author elucidates the importance of various hypotheses in the results by providing counter examples. Features Offers comprehensive coverage of all essential topics required for an introductory course in ODE. Emphasizes on both computation of solutions to ODEs as well as the theoretical concepts like well-posedness comparison results stability etc. Systematic presentation of insights of the nature of the solutions to linear/non-linear ODEs. Special attention on the study of asymptotic behavior of solutions to autonomous ODEs (both for scalar case and 2✕2 systems). Sufficient number of examples are provided wherever a notion is introduced. Contains a rich collection of problems. This book serves as a text book for undergraduate students and a reference book for scientists and engineers. Broad coverage and clear presentation of the material indeed appeals to the readers. Dr. Suman K. Tumuluri has been working in University of Hyderabad India for 11 years and at present he is an associate professor. His research interests include applications of partial differential equations in population dynamics and fluid dynamics.

GBP 82.99
1

Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
1

Linear Algebra and Its Applications with R

Linear Algebra and Its Applications with R

This book developed from the need to teach a linear algebra course to students focused on data science and bioinformatics programs. These students tend not to realize the importance of linear algebra in applied sciences since traditional linear algebra courses tend to cover mathematical contexts but not the computational aspect of linear algebra or its applications to data science and bioinformatics. The author presents the topics in a traditional course yet offers lectures as well as lab exercises on simulated and empirical data sets. This textbook provides students a theoretical basis which can then be applied to the practical R and Python problems providing the tools needed for real-world applications. Each section starts with working examples to demonstrate how tools from linear algebra can help solve problems in applied sciences. These exercises start from easy computations such as computing determinants of matrices to practical applications on simulated and empirical data sets with R so that students learn how to get started with R along with computational examples in each section and then students learn how to apply what they've learned to problems in applied sciences. This book is designed from first principles to demonstrate the importance of linear algebra through working computational examples with R and Python including tutorials on how to install R in the Appendix. If a student has never seen R they can get started without any additional help. Since Python is one of the most popular languages in data science optimization and computer science code supplements are available for students who feel more comfortable with Python. R is used primarily for computational examples to develop students’ practical computational skills. About the Author: Dr. Ruriko Yoshida is an Associate Professor of Operations Research at the Naval Postgraduate School. She received her PhD in Mathematics from the University of California Davis. Her research topics cover a wide variety of areas: applications of algebraic combinatorics to statistical problems such as statistical learning on non-Euclidean spaces sensor networks phylogenetics and phylogenomics. She teaches courses in statistics stochastic models probability and data science. | Linear Algebra and Its Applications with R

GBP 82.99
1

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of interesting – good bad and ugly – features that can be found in data and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data exploratory analysis and R as well as graphics in R. It then explores working with external data linear regression models and crafting data stories. The second part of the book focuses on developing R programs including good programming practices and examples working with text data and general predictive models. The book ends with a chapter on keeping it all together that includes managing the R installation managing files documenting and an introduction to reproducible computing. The book is designed for both advanced undergraduate entry-level graduate students and working professionals with little to no prior exposure to data analysis modeling statistics or programming. it keeps the treatment relatively non-mathematical even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters and an instructor's solution manual is available. About the Author:Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera a property insurance company in Fairfield California and he has previously held similar positions in a variety of application areas including software development drug safety data analysis and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering the Sciences and Medicine (Oxford University Press 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

GBP 44.99
1

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources the Bayesian approach provides a flexible framework for drug development. Despite its advantages the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners Bayesian Analysis with R for Drug Development: Concepts Algorithms and Case Studies describes a wide range of Bayesian applications to problems throughout pre-clinical clinical and Chemistry Manufacturing and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical clinical and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang Ph. D. is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books 15 book chapters and over 90 peer-reviewed papers on diverse scientific and statistical subjects including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick Ph. D. is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences having developed and taught courses in several areas including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences. | Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

GBP 38.99
1

Data Science and Analytics with Python

Data Science and Analytics with Python

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python such as SciKit-learn Pandas Numpy and others. The use of Python is of particular interest given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are from the point of view of the process and results obtained. Important features of Python are also covered including a Python primer. The basic elements of machine learning pattern recognition and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python clustering techniques and classification algorithms are covered in the second part of the book. Hierarchical clustering decision trees and ensemble techniques are also explored along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA IBM Data Science Studio Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London UK and a member of the School of Physics Astronomy and Mathematics at the University of Hertfordshire UK He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave also published by CRC Press. His interests include mathematical modelling data science and optimization in a wide range of applications including optics quantum mechanics data journalism and finance.

GBP 52.99
1

Financial Mathematics Two Volume Set

Financial Mathematics Two Volume Set

This textbook provides complete coverage of discrete-time financial models that form the cornerstones of financial derivative pricing theory. Unlike similar texts in the field this one presents multiple problem-solving approaches linking related comprehensive techniques for pricing different types of financial derivatives. Key features: In-depth coverage of discrete-time theory and methodology. Numerous fully worked out examples and exercises in every chapter. Mathematically rigorous and consistent yet bridging various basic and more advanced concepts. Judicious balance of financial theory mathematical and computational methods. Guide to Material. This revision contains: Almost 200 pages worth of new material in all chapters. A new chapter on elementary probability theory. An expanded the set of solved problems and additional exercises. Answers to all exercises. This book is a comprehensive self-contained and unified treatment of the main theory and application of mathematical methods behind modern-day financial mathematics. Table of Contents List of Figures and Tables Preface I Introduction to Pricing and Management of Financial Securities 1 Mathematics of Compounding 2 Primer on Pricing Risky Securities 3 Portfolio Management 4 Primer on Derivative Securities II Discrete-Time Modelling 5 Single-Period Arrow–Debreu Models 6 Introduction to Discrete-Time Stochastic Calculus 7 Replication and Pricing in the Binomial Tree Model 8 General Multi-Asset Multi-Period Model Appendices A Elementary Probability Theory B Glossary of Symbols and Abbreviations C Answers and Hints to Exercises References Index Biographies Giuseppe Campolieti is Professor of Mathematics at Wilfrid Laurier University in Waterloo Canada. He has been Natural Sciences and Engineering Research Council postdoctoral research fellow and university research fellow at the University of Toronto. In 1998 he joined the Masters in Mathematical Finance as an instructor and later as an adjunct professor in financial mathematics until 2002. Dr. Campolieti also founded a financial software and consulting company in 1998. He joined Laurier in 2002 as Associate Professor of Mathematics and as SHARCNET Chair in Financial Mathematics. Roman N. Makarov is Associate Professor and Chair of Mathematics at Wilfrid Laurier University. Prior to joining Laurier in 2003 he was an Assistant Professor of Mathematics at Siberian State University of Telecommunications and Informatics and a senior research fellow at the Laboratory of Monte Carlo Methods at the Institute of Computational Mathematics and Mathematical Geophysics in Novosibirsk Russia. | Financial Mathematics Two Volume Set

GBP 130.00
1

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
1

The Internet Book Everything You Need to Know about Computer Networking and How the Internet Works

The Internet Book Everything You Need to Know about Computer Networking and How the Internet Works

The Internet Book Fifth Edition explains how computers communicate what the Internet is how the Internet works and what services the Internet offers. It is designed for readers who do not have a strong technical background — early chapters clearly explain the terminology and concepts needed to understand all the services. It helps the reader to understand the technology behind the Internet appreciate how the Internet can be used and discover why people find it so exciting. In addition it explains the origins of the Internet and shows the reader how rapidly it has grown. It also provides information on how to avoid scams and exaggerated marketing claims. The first section of the book introduces communication system concepts and terminology. The second section reviews the history of the Internet and its incredible growth. It documents the rate at which the digital revolution occurred and provides background that will help readers appreciate the significance of the underlying design. The third section describes basic Internet technology and capabilities. It examines how Internet hardware is organized and how software provides communication. This section provides the foundation for later chapters and will help readers ask good questions and make better decisions when salespeople offer Internet products and services. The final section describes application services currently available on the Internet. For each service the book explains both what the service offers and how the service works. About the Author Dr. Douglas Comer is a Distinguished Professor at Purdue University in the departments of Computer Science and Electrical and Computer Engineering. He has created and enjoys teaching undergraduate and graduate courses on computer networks and Internets operating systems computer architecture and computer software. One of the researchers who contributed to the Internet as it was being formed in the late 1970s and 1980s he has served as a member of the Internet Architecture Board the group responsible for guiding the Internet’s development. Prof. Comer is an internationally recognized expert on computer networking the TCP/IP protocols and the Internet who presents lectures to a wide range of audiences. In addition to research articles he has written a series of textbooks that describe the technical details of the Internet. Prof. Comer’s books have been translated into many languages and are used in industry as well as computer science engineering and business departments around the world. Prof. Comer joined the Internet project in the late 1970s and has had a high-speed Internet connection to his home since 1981. He wrote this book as a response to everyone who has asked him for an explanation of the Internet that is both technically correct and easily understood by anyone. An Internet enthusiast Comer displays INTRNET on the license plate of his car. | The Internet Book Everything You Need to Know about Computer Networking and How the Internet Works

GBP 84.99
1

Games Gambling and Probability An Introduction to Mathematics

Games Gambling and Probability An Introduction to Mathematics

Many experiments have shown the human brain generally has very serious problems dealing with probability and chance. A greater understanding of probability can help develop the intuition necessary to approach risk with the ability to make more informed (and better) decisions. The first four chapters offer the standard content for an introductory probability course albeit presented in a much different way and order. The chapters afterward include some discussion of different games different ideas that relate to the law of large numbers and many more mathematical topics not typically seen in such a book. The use of games is meant to make the book (and course) feel like fun! Since many of the early games discussed are casino games the study of those games along with an understanding of the material in later chapters should remind you that gambling is a bad idea; you should think of placing bets in a casino as paying for entertainment. Winning can obviously be a fun reward but should not ever be expected. Changes for the Second Edition: New chapter on Game Theory New chapter on Sports Mathematics The chapter on Blackjack which was Chapter 4 in the first edition appears later in the book. Reorganization has been done to improve the flow of topics and learning. New sections on Arkham Horror Uno and Scrabble have been added. Even more exercises were added! The goal for this textbook is to complement the inquiry-based learning movement. In my mind concepts and ideas will stick with the reader more when they are motivated in an interesting way. Here we use questions about various games (not just casino games) to motivate the mathematics and I would say that the writing emphasizes a just-in-time mathematics approach. Topics are presented mathematically as questions about the games themselves are posed. Table of Contents Preface1. Mathematics and Probability 2. Roulette and Craps: Expected Value 3. Counting: Poker Hands 4. More Dice: Counting and Combinations and Statistics 5. Game Theory: Poker Bluffing and Other Games 6. Probability/Stochastic Matrices: Board Game Movement 7. Sports Mathematics: Probability Meets Athletics 8. Blackjack: Previous Methods Revisited 9. A Mix of Other Games 10. Betting Systems: Can You Beat the System? 11. Potpourri: Assorted Adventures in Probability Appendices Tables Answers and Selected Solutions Bibliography Biography Dr. David G. Taylor is a professor of mathematics and an associate dean for academic affairs at Roanoke College in southwest Virginia. He attended Lebanon Valley College for his B. S. in computer science and mathematics and went to the University of Virginia for his Ph. D. While his graduate school focus was on studying infinite dimensional Lie algebras he started studying the mathematics of various games in order to have a more undergraduate-friendly research agenda. Work done with two Roanoke College students Heather Cook and Jonathan Marino appears in this book! Currently he owns over 100 different board games and enjoys using probability in his decision-making while playing most of those games. In his spare time he enjoys reading cooking coding playing his board games and spending time with his six-year-old dog Lilly. | Games Gambling and Probability An Introduction to Mathematics

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