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David Gold Bayesian Analysis of Gene Expression Data


The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable. This book: Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.

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Группа авторов Case Studies in Bayesian Statistical Modelling and Analysis


Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

8036.47 RUR

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Matthias Dehmer Analysis of Microarray Data


This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: * Understanding and Preprocessing Microarray Data * Clustering of Microarray Data * Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order * Bilayer Verification Algorithm * Probabilistic Boolean Networks as Models for Gene Regulation * Estimating Transcriptional Regulatory Networks by a Bayesian Network * Analysis of Therapeutic Compound Effects * Statistical Methods for Inference of Genetic Networks and Regulatory Modules * Identification of Genetic Networks by Structural Equations * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique as well as those developing new analysis techniques.

21364.02 RUR

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Дэвид Шпигельхалтер Bayesian Approaches to Clinical Trials and Health-Care Evaluation


READ ALL ABOUT IT! David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries. Order a copy of this author’s comprehensive text TODAY! The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques. Illustrated throughout by detailed case studies and worked examples Includes exercises in all chapters Accessible to anyone with a basic knowledge of statistics Authors are at the forefront of research into Bayesian methods in medical research Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS – the most widely used Bayesian modelling package Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.

8670.75 RUR

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Arkady Shemyakin Introduction to Bayesian Estimation and Copula Models of Dependence


Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management Introduction to Bayesian Estimation and Copula Models of Dependence emphasizes the applications of Bayesian analysis to copula modeling and equips readers with the tools needed to implement the procedures of Bayesian estimation in copula models of dependence. This book is structured in two parts: the first four chapters serve as a general introduction to Bayesian statistics with a clear emphasis on parametric estimation and the following four chapters stress statistical models of dependence with a focus of copulas. A review of the main concepts is discussed along with the basics of Bayesian statistics including prior information and experimental data, prior and posterior distributions, with an emphasis on Bayesian parametric estimation. The basic mathematical background of both Markov chains and Monte Carlo integration and simulation is also provided. The authors discuss statistical models of dependence with a focus on copulas and present a brief survey of pre-copula dependence models. The main definitions and notations of copula models are summarized followed by discussions of real-world cases that address particular risk management problems. In addition, this book includes: • Practical examples of copulas in use including within the Basel Accord II documents that regulate the world banking system as well as examples of Bayesian methods within current FDA recommendations • Step-by-step procedures of multivariate data analysis and copula modeling, allowing readers to gain insight for their own applied research and studies • Separate reference lists within each chapter and end-of-the-chapter exercises within Chapters 2 through 8 • A companion website containing appendices: data files and demo files in Microsoft® Office Excel®, basic code in R, and selected exercise solutions Introduction to Bayesian Estimation and Copula Models of Dependence is a reference and resource for statisticians who need to learn formal Bayesian analysis as well as professionals within analytical and risk management departments of banks and insurance companies who are involved in quantitative analysis and forecasting. This book can also be used as a textbook for upper-undergraduate and graduate-level courses in Bayesian statistics and analysis. ARKADY SHEMYAKIN, PhD, is Professor in the Department of Mathematics and Director of the Statistics Program at the University of St. Thomas. A member of the American Statistical Association and the International Society for Bayesian Analysis, Dr. Shemyakin's research interests include informationtheory, Bayesian methods of parametric estimation, and copula models in actuarial mathematics, finance, and engineering. ALEXANDER KNIAZEV, PhD, is Associate Professor and Head of the Department of Mathematics at Astrakhan State University in Russia. Dr. Kniazev's research interests include representation theory of Lie algebras and finite groups, mathematical statistics, econometrics, and financial mathematics.

10764.44 RUR

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Simon Jackman Bayesian Analysis for the Social Sciences


Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.

8313.2 RUR

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Группа авторов Bayesian Models for Categorical Data


The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data – one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.

12246.32 RUR

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Fabrizio Ruggeri Bayesian Analysis of Stochastic Process Models


Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.

8994.95 RUR

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Shay Cohen Bayesian Analysis in Natural Language Processing

Richard Twyman Principles of Gene Manipulation and Genomics


The increasing integration between gene manipulation and genomics is embraced in this new book, Principles of Gene Manipulation and Genomics, which brings together for the first time the subjects covered by the best-selling books Principles of Gene Manipulation and Principles of Genome Analysis & Genomics. Comprehensively revised, updated and rewritten to encompass within one volume, basic and advanced gene manipulation techniques, genome analysis, genomics, transcriptomics, proteomics and metabolomics Includes two new chapters on the applications of genomics An accompanying website – www.blackwellpublishing.com/primrose – provides instructional materials for both student and lecturer use, including multiple choice questions, related websites, and all the artwork in a downloadable format. An essential reference for upper level undergraduate and graduate students of genetics, genomics, molecular biology and recombinant DNA technology.

11620.6 RUR

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William M. Bolstad Introduction to Bayesian Statistics


"…this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.

13102.71 RUR

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Start Publishing Notes Summary, Analysis, and Review of Michael Greger, M.D. and Gene Stone's How Not to Die (Unabridged)


Summary, Analysis, and Review of Summary, Analysis, and Review of Michael Greger, M.D. and Gene Stone's How Not to Die PLEASE NOTE: This is a key takeaways and analysis of the book and NOT the original book. Start Publishing Notes' Summary, Analysis, and Review of Summary, Analysis, and Review of Michael Greger, M.D. and Gene Stone's How Not to Die: Discover the Foods Scientifically Proven to Prevent and Reverse Disease includes a summary of the book, review, analysis & key takeaways, and detailed About the Author section. PREVIEW: How Not to Die: Discover the Foods Scientifically Proven to Prevent and Reverse Disease by Michael Greger, M.D. with Gene Stone asserts that the major causes of disease in the United States are mostly preventable. To encourage Americans to take control of their medical destinies, Greger examines the broken belief systems surrounding food, some specific interventions by disease, and general dietary recommendations. Most Americans hold the false belief that disease is a matter o

1140.17 RUR

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Группа авторов Methods of Biochemical Analysis, Volume 3


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

13408.38 RUR

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Группа авторов Methods of Biochemical Analysis, Volume 10


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

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Группа авторов Methods of Biochemical Analysis, Volume 12


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

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Группа авторов Methods of Biochemical Analysis, Volume 13


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

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Группа авторов Methods of Biochemical Analysis, Volume 14


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

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Группа авторов Methods of Biochemical Analysis, Volume 16


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

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Группа авторов Methods of Biochemical Analysis, Volume 17


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

13408.38 RUR

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Группа авторов Methods of Biochemical Analysis, Volume 23


Biochemical analysis is a rapidly expanding field and is a key component of modern drug discovery and research. Methods of Biochemical Analysis provides a periodic and authoritative review of the latest achievements in biochemical analysis. Founded in 1954 by Professor David Glick, Methods of Biochemical Analysis provides a timely review of the latest developments in the field.

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