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Applied Surrogate Endpoint Evaluation Methods with SAS and R

Applied Surrogate Endpoint Evaluation Methods with SAS and R

An important factor that affects the duration complexity and cost of a clinical trial is the endpoint used to study the treatment’s efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice using SAS and R. | Applied Surrogate Endpoint Evaluation Methods with SAS and R

GBP 44.99
1

Mathematics of Keno and Lotteries

Mathematics of Keno and Lotteries

Mathematics of Keno and Lotteries is an elementary treatment of the mathematics primarily probability and simple combinatorics involved in lotteries and keno. Keno has a long history as a high-advantage high-payoff casino game and state lottery games such as Powerball are mathematically similar. MKL also considers such lottery games as passive tickets daily number drawings and specialized games offered around the world. In addition there is a section on financial mathematics that explains the connection between lump-sum lottery prizes (as with Powerball) and their multi-year annuity options. So-called winning systems for keno and lotteries are examined mathematically and their flaws identified.

GBP 52.99
1

Mind Brain Quantum AI and the Multiverse

Mind Brain Quantum AI and the Multiverse

There is a long-lasting controversy concerning our mind and consciousness. Mind Brain Quantum AI and the Multiverse proposes a connection between the mind the brain and the multiverse. The author introduces the main philosophical ideas concerning mind and freedom and explains the basic principles of computer science artificial intelligence of brain research quantum physics and quantum artificial intelligence. He indicates how we can provide an answer to the problem of the mind and consciousness by describing the nature of the physical world. His proposed explanation includes the Everett Many-Worlds theory. This book tries to avoid any non-essential metaphysical speculations. The text is an essential compilation of knowledge in philosophy computer science biology and quantum physics. It is written for readers without any requirements in mathematics physics or computer science.

GBP 74.99
1

Nonparametric Statistical Inference

Nonparametric Statistical Inference

Praise for previous editions: … a classic with a long history. – Statistical Papers The fact that the first edition of this book was published in 1971 … [is] testimony to the book’s success over a long period. – ISI Short Book Reviews … one of the best books available for a theory course on nonparametric statistics. … very well written and organized … recommended for teachers and graduate students. – Biometrics … There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition. – Technometrics … Useful to students and research workers … a good textbook for a beginning graduate-level course in nonparametric statistics. – Journal of the American Statistical Association Since its first publication in 1971 Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics. The Sixth Edition carries on this tradition and incorporates computer solutions based on R. Features Covers the most commonly used nonparametric procedures States the assumptions develops the theory behind the procedures and illustrates the techniques using realistic examples from the social behavioral and life sciences Presents tests of hypotheses confidence-interval estimation sample size determination power and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R MINITAB STATXACT and SAS Lists over 100 new references Nonparametric Statistical Inference Sixth Edition has been thoroughly revised and rewritten to make it more readable and reader-friendly. All of the R solutions are new and make this book much more useful for applications in modern times. It has been updated throughout and contains 100 new citations including some of the most recent to make it more current and useful for researchers.

GBP 99.99
1

Mixed-Mode Official Surveys Design and Analysis

Mixed-Mode Official Surveys Design and Analysis

Mixed-mode surveys have become a standard at many statistical institutes. However the introduction of multiple modes in one design goes with challenges to both methodology and logistics. Mode-specific representation and measurement differences become explicit and demand for solutions in data collection design questionnaire design and estimation. This is especially true when surveys are repeated and are input to long time series of official statistics. So how can statistical institutes deal with such changes? What are the origins of mode-specific error? And how can they be dealt with? In this book the authors provide answers to these questions and much more. Features Concise introduction to all the key elements of mixed-mode survey design and analysis Realistic official statistics examples from three general population surveys Suitable for survey managers and survey statisticians alike An overview of mode-specific representation and measurement errors and how to avoid reduce and adjust them. | Mixed-Mode Official Surveys Design and Analysis

GBP 44.99
1

Clean Numerical Simulation

Clean Numerical Simulation

This book describes the basic principles of Clean Numerical Simulation (CNS) proposed by the author in 2009 as well as several of its applications. Unlike conventional algorithms CNS gives in a sufficiently long interval of time a convergent chaotic trajectory whose numerical noise is much lower than the true physical solution so that one can gain accurately. Thus CNS provides for the first time an ability to check statistics stability of chaos leading to a completely new concept of ultra-chaos which has both trajectory instability and statistics instability and thus is of a higher disorder. Notably it is impossible to repeat experimental results of ultra-chaos even in the statistical sense. However the reproducibility of physical experiments forms a cornerstone for modern science. Thus ultra-chaos reveals an incompleteness of the modern science paradigm. In addition it also reveals statistics stability as a precondition for use of conventional algorithms including direct numerical simulation (DNS). In Clean Numerical Simulation several conjectures and open problems are proposed including a modified fourth Clay millennium problem. Indeed CNS opens the door for us to enter the clean numerical world of chaos and turbulence.

GBP 74.99
1

Structured Credit Portfolio Analysis Baskets and CDOs

Structured Credit Portfolio Analysis Baskets and CDOs

The financial industry is swamped by credit products whose economic performance is linked to the performance of some underlying portfolio of credit-risky instruments like loans bonds swaps or asset-backed securities. Financial institutions continuously use these products for tailor-made long and short positions in credit risks. Based on a steadily growing market there is a high demand for concepts and techniques applicable to the evaluation of structured credit products. Written from the perspective of practitioners who apply mathematical concepts to structured credit products Structured Credit Portfolio Analysis Baskets & CDOs starts with a brief wrap-up on basic concepts of credit risk modeling and then quickly moves on to more advanced topics such as the modeling and evaluation of basket products credit-linked notes referenced to credit portfolios collateralized debt obligations and index tranches. The text is written in a self-contained style so readers with a basic understanding of probability will have no difficulties following it. In addition many examples and calculations have been included to keep the discussion close to business applications. Practitioners as well as academics will find ideas and tools in the book that they can use for their daily work. | Structured Credit Portfolio Analysis Baskets and CDOs

GBP 59.99
1

Discrete and Continuous Fourier Transforms Analysis Applications and Fast Algorithms

Discrete and Continuous Fourier Transforms Analysis Applications and Fast Algorithms

Long employed in electrical engineering the discrete Fourier transform (DFT) is now applied in a range of fields through the use of digital computers and fast Fourier transform (FFT) algorithms. But to correctly interpret DFT results it is essential to understand the core and tools of Fourier analysis. Discrete and Continuous Fourier Transforms: Analysis Applications and Fast Algorithms presents the fundamentals of Fourier analysis and their deployment in signal processing using DFT and FFT algorithms. This accessible self-contained book provides meaningful interpretations of essential formulas in the context of applications building a solid foundation for the application of Fourier analysis in the many diverging and continuously evolving areas in digital signal processing enterprises. It comprehensively covers the DFT of windowed sequences various discrete convolution algorithms and their applications in digital filtering and filters and many FFT algorithms unified under the frameworks of mixed-radix FFTs and prime factor FFTs. A large number of graphical illustrations and worked examples help explain the concepts and relationships from the very beginning of the text. Requiring no prior knowledge of Fourier analysis or signal processing this book supplies the basis for using FFT algorithms to compute the DFT in a variety of application areas. | Discrete and Continuous Fourier Transforms Analysis Applications and Fast Algorithms

GBP 56.99
1

Legal Analytics The Future of Analytics in Law

Legal Analytics The Future of Analytics in Law

Legal Analytics: The Future of Analytics in Law navigates the crisscrossing of intelligent technology and the legal field in building up a new landscape of transformation. Legal automation navigation is multidimensional wherein it intends to construct streamline communication approval and management of legal tasks. The evolving environment of technology has emphasized the need for better automation in the legal field from time to time although legal scholars took long to embrace information revolution of the legal field. • Describes the historical development of law and automation. • Analyzes the challenges and opportunities in law and automation. • Studies the current research and development in the convergence of law artificial intelligence and legal analytics. • Explores the recent emerging trends and technologies that are used by various legal systems globally for crime prediction and prevention. • Examines the applicability of legal analytics in forensic investigation. • Investigates the impact of legal analytics tools and techniques in judicial decision making. • Analyzes deep learning techniques and their scope in accelerating legal analytics in developed and developing countries. • Provides an in-depth analysis of implementation challenges and issues in society related to legal analytics. This book is primarily aimed at graduates and postgraduates in law and technology computer science and information technology. Legal practitioners and academicians will also find this book helpful. | Legal Analytics The Future of Analytics in Law

GBP 140.00
1

Nonlinear Time Series Semiparametric and Nonparametric Methods

Nonlinear Time Series Semiparametric and Nonparametric Methods

Useful in the theoretical and empirical analysis of nonlinear time series data semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully nonparametric models and methods. Answering the call for an up-to-date overview of the latest developments in the field Nonlinear Time Series: Semiparametric and Nonparametric Methods focuses on various semiparametric methods in model estimation specification testing and selection of time series data. After a brief introduction the book examines semiparametric estimation and specification methods and then applies these approaches to a class of nonlinear continuous-time models with real-world data. It also assesses some newly proposed semiparametric estimation procedures for time series data with long-range dependence. Even though the book only deals with climatological and financial data the estimation and specifications methods discussed can be applied to models with real-world data in many disciplines. This resource covers key methods in time series analysis and provides the necessary theoretical details. The latest applied finance and financial econometrics results and applications presented in the book enable researchers and graduate students to keep abreast of developments in the field. | Nonlinear Time Series Semiparametric and Nonparametric Methods

GBP 59.99
1

A Primer on Wavelets and Their Scientific Applications

A Primer on Wavelets and Their Scientific Applications

In the first edition of his seminal introduction to wavelets James S. Walker informed us that the potential applications for wavelets were virtually unlimited. Since that time thousands of published papers have proven him true while also necessitating the creation of a new edition of his bestselling primer. Updated and fully revised to include the latest developments this second edition of A Primer on Wavelets and Their Scientific Applications guides readers through the main ideas of wavelet analysis in order to develop a thorough appreciation of wavelet applications. Ingeniously relying on elementary algebra and just a smidgen of calculus Professor Walker demonstrates how the underlying ideas behind wavelet analysis can be applied to solve significant problems in audio and image processing as well in biology and medicine. Nearly twice as long as the original this new edition provides 104 worked examples and 222 exercises constituting a veritable book of review material Two sections on biorthogonal wavelets A mini-course on image compression including a tutorial on arithmetic compression Extensive material on image denoising featuring a rarely covered technique for removing isolated randomly positioned clutter Concise yet complete coverage of the fundamentals of time-frequency analysis showcasing its application to audio denoising and musical theory and synthesis An introduction to the multiresolution principle a new mathematical concept in musical theory Expanded suggestions for research projects An enhanced list of references

GBP 180.00
1

Handbook of Discrete-Valued Time Series

Handbook of Discrete-Valued Time Series

Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts some of the techniques discussed can be applied to other types of discrete-valued time series such as binary-valued or categorical time series. Explore a Balanced Treatment of Frequentist and Bayesian Perspectives Accessible to graduate-level students who have taken an elementary class in statistical time series analysis the book begins with the history and current methods for modeling and analyzing univariate count series. It next discusses diagnostics and applications before proceeding to binary and categorical time series. The book then provides a guide to modern methods for discrete-valued spatio-temporal data illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series. Get Guidance from Masters in the FieldWritten by a cohesive group of distinguished contributors this handbook provides a unified account of the diverse techniques available for observation- and parameter-driven models. It covers likelihood and approximate likelihood methods estimating equations simulation methods and a Bayesian approach for model fitting.

GBP 66.99
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Algebraic Number Theory and Fermat's Last Theorem

Algebraic Number Theory and Fermat's Last Theorem

Updated to reflect current research Algebraic Number Theory and Fermat’s Last Theorem Fourth Edition introduces fundamental ideas of algebraic numbers and explores one of the most intriguing stories in the history of mathematics—the quest for a proof of Fermat’s Last Theorem. The authors use this celebrated theorem to motivate a general study of the theory of algebraic numbers from a relatively concrete point of view. Students will see how Wiles’s proof of Fermat’s Last Theorem opened many new areas for future work. New to the Fourth EditionProvides up-to-date information on unique prime factorization for real quadratic number fields especially Harper’s proof that Z(√14) is EuclideanPresents an important new result: Mihăilescu’s proof of the Catalan conjecture of 1844Revises and expands one chapter into two covering classical ideas about modular functions and highlighting the new ideas of Frey Wiles and others that led to the long-sought proof of Fermat’s Last TheoremImproves and updates the index figures bibliography further reading list and historical remarksWritten by preeminent mathematicians Ian Stewart and David Tall this text continues to teach students how to extend properties of natural numbers to more general number structures including algebraic number fields and their rings of algebraic integers. It also explains how basic notions from the theory of algebraic numbers can be used to solve problems in number theory. | Algebraic Number Theory and Fermat's Last Theorem

GBP 39.99
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Combinatorics of Permutations

Combinatorics of Permutations

A CHOICE Outstanding Academic Title the first edition of this bestseller was lauded for its detailed yet engaging treatment of permutations. Providing more than enough material for a one-semester course Combinatorics of Permutations third edition continues to clearly show the usefulness of this subject for both students and researchers. The research in combinatorics of permutations has advanced rapidly since this book was published in a first edition. Now the third edition offers not only updated results it remains the leading textbook for a course on the topic. Coverage is mostly enumerative but there are algebraic analytic and topological parts as well and applications. Since the publication of the second edition there is tremendous progress in pattern avoidance (Chapters 4 and 5). There is also significant progress in the analytic combinatorics of permutations which will be incorporated. •A completely new technique from extremal combinatorics disproved a long-standing conjecture and this is presented in Chapter 4. •The area of universal permutations has undergone a lot of very recent progress and that has been noticed outside the academic community as well. This also influenced the revision of Chapter 5. •New results in stack sorting are added to Chapter 8. •Chapter 9 applications to biology has been revised. The author’s other works include Introduction to Enumerative and Analytic Combinatorics second edition (CHOICE Outstanding Academic Title) and Handbook of Enumerative Combinatorics published by CRC Press. The author also serves as Series Editor for CRC’s Discrete Mathematics and Its Applications.

GBP 99.99
1

Achieving Product Reliability A Key to Business Success

Achieving Product Reliability A Key to Business Success

Are you buying a car or smartphone or dishwasher? We bet long-term trouble-free operation (i. e. high reliability) is among the top three things you look for. Reliability problems can lead to everything from minor inconveniences to human disasters. Ensuring high reliability in designing and building manufactured products is principally an engineering challenge–but statistics plays a key role. Achieving Product Reliability explains in a non-technical manner how statistics is used in modern product reliability assurance. Features: Describes applications of statistics in reliability assurance in design development validation manufacturing and field tracking. Uses real-life examples to illustrate key statistical concepts such as the Weibull and lognormal distributions hazard rate and censored data. Demonstrates the use of graphical tools in such areas as accelerated testing degradation data modeling and repairable systems data analysis. Presents opportunities for profitably applying statistics in the era of Big Data and Industrial Internet of Things (IIoT) utilizing for example the instantaneous transmission of large quantities of field data. Whether you are an intellectually curious citizen student manager budding reliability professional or academician seeking practical applications Achieving Product Reliability is a great starting point for a big-picture view of statistics in reliability assurance. The authors are world-renowned experts on this topic with extensive experience as company-wide statistical resources for a global conglomerate consultants to business and government and researchers of statistical methods for reliability applications. | Achieving Product Reliability A Key to Business Success

GBP 31.99
1

Introduction to Functional Data Analysis

Introduction to Functional Data Analysis

Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors as well as for MS and PhD students in other disciplines including applied mathematics environmental science public health medical research geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems. The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA 2) functional regression models 3) sparse and dependent functional data and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus linear algebra distributional probability theory foundations of statistical inference and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.

GBP 44.99
1

Bayes Rules An Introduction to Applied Bayesian Modeling

Bayes Rules An Introduction to Applied Bayesian Modeling

An engaging sophisticated and fun introduction to the field of Bayesian statistics Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking modeling and computing to a broad audience. In particular the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. the book assumes that readers are familiar with the content covered in a typical undergraduate-level introductory statistics course. Readers will also ideally have some experience with undergraduate-level probability calculus and the R statistical software. Readers without this background will still be able to follow along so long as theyare eager to pick up these tools on the fly as all R code is provided. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory. | Bayes Rules An Introduction to Applied Bayesian Modeling

GBP 59.99
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Artificial Superintelligence A Futuristic Approach

Artificial Superintelligence A Futuristic Approach

A day does not go by without a news article reporting some amazing breakthrough in artificial intelligence (AI). Many philosophers futurists and AI researchers have conjectured that human-level AI will be developed in the next 20 to 200 years. If these predictions are correct it raises new and sinister issues related to our future in the age of intelligent machines. Artificial Superintelligence: A Futuristic Approach directly addresses these issues and consolidates research aimed at making sure that emerging superintelligence is beneficial to humanity. While specific predictions regarding the consequences of superintelligent AI vary from potential economic hardship to the complete extinction of humankind many researchers agree that the issue is of utmost importance and needs to be seriously addressed. Artificial Superintelligence: A Futuristic Approach discusses key topics such as: AI-Completeness theory and how it can be used to see if an artificial intelligent agent has attained human level intelligence Methods for safeguarding the invention of a superintelligent system that could theoretically be worth trillions of dollars Self-improving AI systems: definition types and limits The science of AI safety engineering including machine ethics and robot rights Solutions for ensuring safe and secure confinement of superintelligent systems The future of superintelligence and why long-term prospects for humanity to remain as the dominant species on Earth are not great Artificial Superintelligence: A Futuristic Approach is designed to become a foundational text for the new science of AI safety engineering. AI researchers and students computer security researchers futurists and philosophers should find this an invaluable resource. | Artificial Superintelligence A Futuristic Approach

GBP 180.00
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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression including models for right-skewed categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors. Features: Presents the theoretical framework for each regression. Discusses data that are categorical count proportions right-skewed longitudinal and hierarchical. Uses examples based on real-life consulting projects. Provides complete SAS and R codes for each example. Includes several exercises for every regression. Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required. The Author:Olga Korosteleva is a Professor of Statistics at California State University Long Beach. She teaches a large variety of statistical courses to undergraduate and master’s students. She has published three statistical textbooks. For a number of years she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences nursing kinesiology and other fields.

GBP 44.99
1

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem social and infrastructure resilience given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation climate modeling and long-term prediction; approach the problems of increasing frequency of extreme events sea level rise and forest fires as well as economic losses analysis of climate impacts for insurance agriculture fisheries and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling statistics and machine learning including the global circulation models (GCM) and ocean models statistical generalized additive models (GAM) and generalized linear models (GLM) state space and graphical models causality networks Bayesian ensembles a variety of index methods and statistical tests and machine learning methods. The reader will learn about data from various sources including GCM and ocean model outputs satellite observations and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

GBP 54.99
1

A Course in Categorical Data Analysis

A Course in Categorical Data Analysis

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

GBP 170.00
1

Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy suitability and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms architecture design and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model performance evaluation and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning along with recent research developments in healthcare sectors. | Machine Learning for Healthcare Handling and Managing Data

GBP 115.00
1

Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks: nonparametric methods data exploration with predictive models penalized regressions model selection with sparsity dimension reduction methods nonparametric time-series predictions graphical network analysis algorithmic optimization methods classification with imbalanced data and many others. This book is targeted at students and researchers who have no advanced statistical background but instead coming from the tradition of inferential statistics. The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields. Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in estimation and prediction for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It’s self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions Dimension Reduction Methods and Graphical Methods have been increasing in their popularity in social sciences. | Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

GBP 74.99
1

Unmatched 50 Years of Supercomputing

Unmatched 50 Years of Supercomputing

Unmatched: 50 Years of Supercomputing: A Personal Journey Accompanying the Evolution of a Powerful Tool The rapid and extraordinary progress of supercomputing over the past half-century is a powerful demonstration of our relentless drive to understand and shape the world around us. In this book David Barkai offers a unique and compelling account of this remarkable technological journey drawing from his own rich experiences working at the forefront of high-performance computing (HPC). This book is a journey delineated as five decade-long ‘epochs’ defined by the systems’ architectural themes: vector processors multi-processors microprocessors clusters and accelerators and cloud computing. The final part examines key issues of HPC and discusses where it might be headed. A central goal of this book is to show how computing power has been applied and more importantly how it has impacted and benefitted society. To this end the use of HPC is illustrated in a range of industries and applications from weather and climate modeling to engineering and life sciences. As such this book appeals to both students and general readers with an interest in HPC as well as industry professionals looking to revolutionize their practice. From the Foreword: “David Barkai's career has spanned five decades during which he has had the rare opportunity to be part of some of the most significant developments in the field of supercomputing. His personal and professional insights combined with his deep knowledge and passion for the subject matter make this book an invaluable resource for anyone interested in the evolution of HPC and its impact on our lives. ” -Horst Simon Director Abu Dhabi Investment Authority (ADIA) Lab | Unmatched 50 Years of Supercomputing

GBP 45.99
1

Image Processing and Acquisition using Python

Image Processing and Acquisition using Python

Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together. By improving readers’ knowledge of image acquisition techniques and corresponding image processing the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn Python is used in a variety of practical examples. A refresher for more experienced readers the first part of the book presents an introduction to Python Python modules reading and writing images using Python and an introduction to images. The second part discusses the basics of image processing including pre/post processing using filters segmentation morphological operations and measurements. The second part describes image acquisition using various modalities such as x-ray CT MRI light microscopy and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry. Features Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images. Contains many examples detailed derivations and working Python examples of the techniques. Offers practical tips on image acquisition and processing. Includes numerous exercises to test the reader’s skills in Python programming and image processing with solutions to selected problems example programs and images available on the book’s web page. New to this edition Machine learning has become an indispensable part of image processing and computer vision so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks. A new chapter on affine transform and many new algorithms. Updated Python code aligned to the latest version of modules.

GBP 44.99
1