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    Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines.Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications.The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance.The text provides comprehensive coverage of computer-intensive applications, with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods Makes it easy for biologists, researchers, and students to understand the methods used Provides information about computer programs and packages to implement calculations, particularly using R code Includes a large number of real examples from a range of biological disciplinesWritten in an accessible style, with minimal coverage of theoretical details, this book provides an excellent introduction to computer-intensive statistical methods for biological researchers.It can be used as a course text for graduate students, as well as a reference for researchers from a range of disciplines.The detailed, worked examples of real applications will enable practitioners to apply the methods to their own biological data.

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  • Mendelian Randomization : Methods for Causal Inference Using Genetic Variants
    Mendelian Randomization : Methods for Causal Inference Using Genetic Variants

    Mendelian Randomization: Methods For Causal Inference Using Genetic Variants provides thorough coverage of the methods and practical elements of Mendelian randomization analysis.It brings together diverse aspects of Mendelian randomization from the fields of epidemiology, statistics, genetics, and bioinformatics. Through multiple examples, the first part of the book introduces the reader to the concept of Mendelian randomization, showing how to perform simple Mendelian randomization investigations and interpret the results.The second part of the book addresses specific methodological issues relevant to the practice of Mendelian randomization, including robust methods, weak instruments, multivariable methods, and power calculations.The authors present the theoretical aspects of these issues in an easy-to-understand way by using non-technical language.The last part of the book examines the potential for Mendelian randomization in the future, exploring both methodological and applied developments. FeaturesOffers first-hand, in-depth guidance on Mendelian randomization from leaders in the fieldMakes the diverse aspects of Mendelian randomization understandable to newcomersIllustrates technical details using data from applied analysesDiscusses possible future directions for research involving Mendelian randomizationSoftware code is provided in the relevant chapters and is also available at the supplementary websiteThis book gives epidemiologists, statisticians, geneticists, and bioinformaticians the foundation to understand how to use genetic variants as instrumental variables in observational data. New in Second Edition: The second edition of the book has been substantially re-written to reduce the amount of technical content, and emphasize practical consequences of theoretical issues.Extensive material on the use of two-sample Mendelian randomization and publicly-available summarized data has been added.The book now includes several real-world examples that show how Mendelian randomization can be used to address questions of disease aetiology, target validation, and drug development

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  • Probability and Computing : Randomization and Probabilistic Techniques in Algorithms and Data Analysis
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    Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science.Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma.Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications.Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems.This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.

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    ?This open access book is authored by a rich mix of contributors from across the landscape of research, academia, LEAs, civil protection, and other first responders, practitioners, public and private organizations and industry to address some of the most contemporary challenges within global security domain.The authors cover topics such as protection of critical infrastructures (CI), serious and organized crime, counter (cyber) terrorism, border management, cybercrime, cybersecurity, management of disasters and crises, big data analytics, the application of AI and the legal and ethical dimensions of the implementation of cutting-edge technologies.The book benefits from research actions and lessons learned from more than 35 EU R&D projects within the security domain.The book not only addresses theoretical narratives pertinent to the subject but also identifies current challenges and emerging security threats, provides analysis of operational capability gaps, real world applied solutions, methodologies, and case studies within security domain. This is an open access book.

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    This book provides a step-by-step guidance on how to implement analytical methods in project risk management.The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I theauthors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects.The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.

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  • Engineering Project Management
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