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Bayesian Data Analysis for Animal Scientists, Agust?n Blasco


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Автор: Agust?n Blasco
Название:  Bayesian Data Analysis for Animal Scientists
ISBN: 9783319853598
Издательство: Springer
Классификация:




ISBN-10: 3319853597
Обложка/Формат: Soft cover
Страницы: 275
Вес: 0.62 кг.
Дата издания: 2017
Язык: English
Издание: Softcover reprint of
Иллюстрации: 10 tables, color; 151 illustrations, color; 9 illustrations, black and white; xviii, 275 p. 160 illus., 151 illus. in color.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Life Sciences
Подзаголовок: The Basics
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters.

Linear Algebra and Learning from Data

Автор: Strang Gilbert
Название: Linear Algebra and Learning from Data
ISBN: 0692196382 ISBN-13(EAN): 9780692196380
Издательство: Cambridge Academ
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Цена: 66520.00 T
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Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists

Автор: Natasa Przulj
Название: Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists
ISBN: 1108432239 ISBN-13(EAN): 9781108432238
Издательство: Cambridge Academ
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Цена: 51750.00 T
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Описание: Bringing together leading experts in the field of network data analysis, this text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning. Using real-world biological and medical examples, applications of these theories are discussed and creative thinking is encouraged in the analysis of such complex network data sets.

The Analysis of Biological Data

Автор: Michael C. Whitlock, Dolph Schluter
Название: The Analysis of Biological Data
ISBN: 1319325343 ISBN-13(EAN): 9781319325343
Издательство: Macmillan Learning
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Цена: 92390.00 T
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Описание: The evolution of a classicThe new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level.

Bayesian Data Analysis in Ecology Using Linear Models with R, BUG

Автор: Franzi Korner-Nievergelt
Название: Bayesian Data Analysis in Ecology Using Linear Models with R, BUG
ISBN: 0128013702 ISBN-13(EAN): 9780128013700
Издательство: Elsevier Science
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Цена: 66240.00 T
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Описание:

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions--including all R codes--that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.


Numerical Analysis for Engineers and Scientists

Автор: Miller
Название: Numerical Analysis for Engineers and Scientists
ISBN: 1107021081 ISBN-13(EAN): 9781107021082
Издательство: Cambridge Academ
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Цена: 70750.00 T
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Описание: Striking a balance between theory and practice, this graduate-level text is perfect for students in the applied sciences. It provides full coverage of classical methods with a clear explanation of how they work, together with sample programs and many practical examples to help students get started.

Numerical Methods in Engineering with MATLAB

Автор: Kiusalaas
Название: Numerical Methods in Engineering with MATLAB
ISBN: 1107120578 ISBN-13(EAN): 9781107120570
Издательство: Cambridge Academ
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Цена: 92930.00 T
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Описание: The third edition of this text describes a range of widely used numerical methods, with an emphasis on problem solving. Every method is discussed thoroughly and illustrated with problems involving both hand computation and programming. MATLAB (R) M-files accompany each method and are available online, as are solutions to the problems.

Bayesian Probability Theory

Автор: Linden
Название: Bayesian Probability Theory
ISBN: 1107035902 ISBN-13(EAN): 9781107035904
Издательство: Cambridge Academ
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Цена: 91860.00 T
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Описание: Covering all aspects of probability theory, statistics and data analysis from a Bayesian perspective, this book is ideal for graduate students and researchers. It presents the roots, applications and numerical implementation of probability theory, covers advanced topics and features real-world problems.

Practical Bayesian Inference

Автор: Bailer-Jones Coryn A L
Название: Practical Bayesian Inference
ISBN: 1316642216 ISBN-13(EAN): 9781316642214
Издательство: Cambridge Academ
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Цена: 40120.00 T
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Описание: This volume introduces the major concepts of probability and statistics and the computational tools students need to extract information from data in the presence of uncertainty. Using a simple and intuitive Bayesian approach, the emphasis throughout is on the principles and showing how these methods can be implemented in practice.

Practical Bayesian Inference: A Primer for Physical Scientists

Автор: Bailer-Jones Coryn A. L.
Название: Practical Bayesian Inference: A Primer for Physical Scientists
ISBN: 1107192110 ISBN-13(EAN): 9781107192119
Издательство: Cambridge Academ
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Цена: 90810.00 T
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Описание: This volume introduces the major concepts of probability and statistics and the computational tools students need to extract information from data in the presence of uncertainty. Using a simple and intuitive Bayesian approach, the emphasis throughout is on the principles and showing how these methods can be implemented in practice.

How to Tame a Fox (and Build a Dog): Visionary Scientists and a Siberian Tale of Jump-Started Evolution

Автор: Dugatkin Lee Alan, Trut Lyudmila
Название: How to Tame a Fox (and Build a Dog): Visionary Scientists and a Siberian Tale of Jump-Started Evolution
ISBN: 022659971X ISBN-13(EAN): 9780226599717
Издательство: Wiley
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Цена: 19010.00 T
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Описание: The crazy story of how Soviet scientists in Siberia in the 1950s embarked on a project to turn foxes into dogs through selective breeding-and managed it within mere generations.

Structure-Preserving Doubling Algorithms for Nonlinear Matrix Equations

Автор: Tsung-Ming Huang, Ren-Cang Li, Wen-Wei Lin
Название: Structure-Preserving Doubling Algorithms for Nonlinear Matrix Equations
ISBN: 1611975352 ISBN-13(EAN): 9781611975352
Издательство: Mare Nostrum (Eurospan)
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Цена: 53090.00 T
Наличие на складе: Невозможна поставка.
Описание: Nonlinear matrix equations arise frequently in applied science and engineering. This is the first book to provide a unified treatment of structure-preserving doubling algorithms, which have been recently studied and proven effective for notoriously challenging problems, such as fluid queue theory and vibration analysis for high-speed trains. The authors present recent developments and results for the theory of doubling algorithms for nonlinear matrix equations associated with regular matrix pencils, and highlight the use of these algorithms in achieving robust solutions for notoriously challenging problems that other methods cannot.Structure-Preserving Doubling Algorithms for Nonlinear Matrix Equations is intended for researchers and computational scientists. Graduate students may also find it of interest.

Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science

Автор: Thomas Huckle, Tobias Neckel
Название: Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science
ISBN: 1611975557 ISBN-13(EAN): 9781611975550
Издательство: Mare Nostrum (Eurospan)
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Цена: 38450.00 T
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Описание: In scientific computing (also known as computational science), advanced computing capabilities are used to solve complex problems. This self-contained book describes and analyzes reported software failures related to the major topics within scientific computing: mathematical modeling of phenomena; numerical analysis (number representation, rounding, conditioning); mathematical aspects and complexity of algorithms, systems, or software; concurrent computing (parallelization, scheduling, synchronization); and numerical data (such as input of data and design of control logic).Readers will find lists of related, interesting bugs, MATLAB examples, and ""excursions"" that provide necessary background, as well as an in-depth analysis of various aspects of the selected bugs. Illustrative examples of numerical principles such as machine numbers, rounding errors, condition numbers, and complexity are also included.This book is intended for students, teachers, and researchers in scientific computing, computer science, and applied mathematics. It is also an entertaining and motivating introduction for those with a minimum background in mathematics or computer science. Bits and Bugs can be used for courses in numerical analysis, numerical methods in linear algebra/ODEs/PDEs, introductory software engineering, introductory scientific computing, and parallel programming.


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