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Chromatin Structure Dynamics Regulation

Chromatin Structure Dynamics Regulation

An invaluable resource for computational biologists and researchers from other fields seeking an introduction to the topic Chromatin: Structure Dynamics Regulation offers comprehensive coverage of this dynamic interdisciplinary field from the basics to the latest research. Computational methods from statistical physics and bioinformatics are detailed whenever possible without lengthy recourse to specialized techniques. | Chromatin Structure Dynamics Regulation

GBP 44.99
1

Basics of Matrix Algebra for Statistics with R

Basics of Matrix Algebra for Statistics with R

A Thorough Guide to Elementary Matrix Algebra and Implementation in RBasics of Matrix Algebra for Statistics with R provides a guide to elementary matrix algebra sufficient for undertaking specialized courses such as multivariate data analysis and linear models. It also covers advanced topics such as generalized inverses of singular and rectangular matrices and manipulation of partitioned matrices for those who want to delve deeper into the subject. The book introduces the definition of a matrix and the basic rules of addition subtraction multiplication and inversion. Later topics include determinants calculation of eigenvectors and eigenvalues and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques including principal component analysis canonical correlation analysis and linear modeling. In addition to the algebraic manipulation of matrices the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers. Avoiding vector spaces and other advanced mathematics this book shows how to manipulate matrices and perform numerical calculations in R. It prepares readers for higher-level and specialized studies in statistics.

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

Correspondence Analysis in Practice

Correspondence Analysis in Practice

Drawing on the author’s 45 years of experience in multivariate analysis Correspondence Analysis in Practice Third Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants subset CA multiple CA and joint CA translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social environmental and health sciences as well as marketing economics linguistics archaeology and more. Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra Barcelona Spain where he teaches a course amongst others on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters mostly on correspondence analysis the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists sociologists data scientists and marketing professionals and has specialized in statistics in ecology and social science.

GBP 48.99
1

Handbook of Statistical Distributions with Applications

Handbook of Statistical Distributions with Applications

Easy-to-Use Reference and Software for Statistical Modeling and TestingHandbook of Statistical Distributions with Applications Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations. Along with many new examples and results this edition includes both the author’s StatCalc software and R codes to accurately and easily carry out computations. New to the Second Edition Major changes in binomial Poisson normal gamma Weibull exponential logistic Laplace and Pareto distributions Updated statistical tests and intervals based on recent publications in statistical journals Enhanced PC calculator StatCalc with electronic help manualsR functions for cases where StatCalc is not applicable with the codes available onlineThis highly praised handbook integrates popular probability distribution models formulas applications and software to help you compute a variety of statistical intervals. It covers probability and percentiles algorithms for random number generation hypothesis tests confidence intervals tolerance intervals prediction intervals sample size determination and much more.

GBP 44.99
1

A Theoretical Introduction to Numerical Analysis

A Theoretical Introduction to Numerical Analysis

A Theoretical Introduction to Numerical Analysis presents the general methodology and principles of numerical analysis illustrating these concepts using numerical methods from real analysis linear algebra and differential equations. The book focuses on how to efficiently represent mathematical models for computer-based study. An accessible yet rigorous mathematical introduction this book provides a pedagogical account of the fundamentals of numerical analysis. The authors thoroughly explain basic concepts such as discretization error efficiency complexity numerical stability consistency and convergence. The text also addresses more complex topics like intrinsic error limits and the effect of smoothness on the accuracy of approximation in the context of Chebyshev interpolation Gaussian quadratures and spectral methods for differential equations. Another advanced subject discussed the method of difference potentials employs discrete analogues of Calderon’s potentials and boundary projection operators. The authors often delineate various techniques through exercises that require further theoretical study or computer implementation. By lucidly presenting the central mathematical concepts of numerical methods A Theoretical Introduction to Numerical Analysis provides a foundational link to more specialized computational work in fluid dynamics acoustics and electromagnetism.

GBP 59.99
1

Introduction to Randomized Controlled Clinical Trials

Introduction to Randomized Controlled Clinical Trials

Evidence from randomized controlled clinical trials is widely accepted as the only sound basis for assessing the efficacy of new medical treatments. Statistical methods play a key role in all stages of these trials including their justification design and analysis. This second edition of Introduction to Randomized Controlled Clinical Trials provides a concise presentation of the principles applied in this area. It details the concepts behind randomization and methods for designing and analyzing trials and also includes information on meta-analysis and specialized designs such as cross-over trials cluster-randomized designs and equivalence studies. This latest edition features new and revised references examples exercises and a new chapter dedicated to binary outcomes and survival analysis. It also presents numerous examples taken from the medical literature contains exercises at the end of each chapter and offers solutions in an appendix. The author uses Minitab and R software throughout the text for implementing the methods that are presented. Comprehensive and accessible Introduction to Randomized Controlled Clinical Trials is well-suited for those familiar with elementary statistical ideas and methods who want to further their knowledge of the subject.

GBP 180.00
1

Discrete Event Simulation for Health Technology Assessment

Discrete Event Simulation for Health Technology Assessment

Discover How to Apply DES to Problems Encountered in HTADiscrete event simulation (DES) has traditionally been used in the engineering and operations research fields. The use of DES to inform decisions about health technologies is still in its infancy. Written by specialists at the forefront of this area Discrete Event Simulation for Health Technology Assessment is the first book to make all the central concepts of DES relevant for health technology assessment (HTA). Accessible to beginners the book requires no prerequisites and describes the concepts with as little jargon as possible. The book first covers the essential concepts and their implementation. It next provides a fully worked out example using both a widely available spreadsheet program (Microsoft Excel) and a popular specialized simulation package (Arena). It then presents approaches to analyze the simulations including the treatment of uncertainty; tackles the development of the required equations; explains the techniques to verify that the models are as efficient as possible; and explores the indispensable topic of validation. The book also covers a variety of non-essential yet handy topics such as the animation of a simulation and extensions of DES and incorporates a real case study involving screening strategies for breast cancer surveillance. This book guides you in leveraging DES in your assessments of health technologies. After reading the chapters in sequence you will be able to construct a realistic model designed to help in the assessment of a new health technology.

GBP 44.99
1

Component-Based Software Engineering Methods and Metrics

Component-Based Software Engineering Methods and Metrics

This book focuses on a specialized branch of the vast domain of software engineering: component-based software engineering (CBSE). Component-Based Software Engineering: Methods and Metrics enhances the basic understanding of components by defining categories characteristics repository interaction complexity and composition. It divides the research domain of CBSE into three major sub-domains: (1) reusability issues (2) interaction and integration issues and (3) testing and reliability issues. This book covers the state-of-the-art literature survey of at least 20 years in the domain of reusability interaction and integration complexities and testing and reliability issues of component-based software engineering. The aim of this book is not only to review and analyze the previous works conducted by eminent researchers academicians and organizations in the context of CBSE but also suggests innovative efficient and better solutions. A rigorous and critical survey of traditional and advanced paradigms of software engineering is provided in the book. Features: In-interactions and Out-Interactions both are covered to assess the complexity. In the context of CBSE both white-box and black-box testing methods and their metrics are described. This work covers reliability estimation using reusability which is an innovative method. Case studies and real-life software examples are used to explore the problems and their solutions. Students research scholars software developers and software designers or individuals interested in software engineering especially in component-based software engineering can refer to this book to understand the concepts from scratch. These measures and metrics can be used to estimate the software before the actual coding commences. | Component-Based Software Engineering Methods and Metrics

GBP 105.00
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

Handbook of Meta-Analysis

Handbook of Meta-Analysis

Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas including medicine and health social sciences education psychology ecology and economics. Recent years have seen an explosion of methods for handling complexities in meta-analysis including explained and unexplained heterogeneity between studies publication bias and sparse data. At the same time meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups to complex observational studies to assessing heterogeneity of effects and to survival and multivariate outcomes. Many of these methods are statistically complex and are tailored to specific types of data. Key features Rigorous coverage of the full range of current statistical methodology used in meta-analysis Comprehensive coherent and unified overview of the statistical foundations behind meta-analysis Detailed description of the primary methods for both univariate and multivariate data Computer code to reproduce examples in chapters Thorough review of the literature with thousands of references Applications to specific types of biomedical and social science data Supplementary website with code data sample chapters and errata This book is for a broad audience of graduate students researchers and practitioners interested in the theory and application of statistical methods for meta-analysis. It is written at the level of graduate courses in statistics but will be of interest to and readable for quantitative scientists from a range of disciplines. The book can be used as a graduate level textbook as a general reference for methods or as an introduction to specialized topics using state-of-the art methods. | Handbook of Meta-Analysis

GBP 59.99
1

Computational Genomics with R

Computational Genomics with R

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming to machine learning and statistics to the latest genomic data analysis techniques. The text provides accessible information and explanations always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary it requires different starting points for people with different backgrounds. For example a biologist might skip sections on basic genome biology and start with R programming whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics supervised and unsupervised learning techniques that are important in data modeling and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics such as heatmaps meta-gene plots and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets such as RNA-seq ChIP-seq and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology Max Delbrück Center Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

GBP 42.99
1