Автор: Peacock, Janet L. Название: Oxford handbook of medical statistics 2e ISBN: 0198743580 ISBN-13(EAN): 9780198743583 Издательство: Oxford Academ Рейтинг: Цена: 5384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This new edition of the Oxford Handbook of Medical Statistics provides doctors and medical students with a clear and concise explanation of statistical methods. It is an accessible and thorough account of a complex subject, and the previous edition has been described by readers as a `statistical Bible`.
Автор: Krickeberg Klaus, Van Trong Pham, Thi My Hanh Pham Название: Epidemiology: Key to Public Health. 2 ed. ISBN: 3030163679 ISBN-13(EAN): 9783030163679 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: ?This unique textbook presents the field of modern epidemiology as a whole; it does not restrict itself to particular aspects. It stresses the fundamental ideas and their role in any situation of epidemiologic practice. Its structure is largely determined by didactic viewpoints.Epidemiology is the art of defining and investigating the influence of factors on the health of populations. Hence the book starts by sketching the role of epidemiology in public health. It then treats the epidemiology of many particular diseases; mathematical modelling of epidemics and immunity; health information systems; statistical methods and sample surveys; clinical epidemiology including clinical trials; nutritional, environmental, social, and genetic epidemiology; and the habitual tools of epidemiologic studies. The book also reexamines the basic difference between the epidemiology of infectious diseases and that of non-infectious ones.The organization of the topics by didactic aspects makes the book ideal for teaching. All examples and case studies are situated in a single country, namely Vietnam; this provides a particularly vivid picture of the role of epidemiology in shaping the health of a population. It can easily be adapted to other developing or transitioning countries.This volume is well suited for courses on epidemiology and public health at the upper undergraduate and graduate levels, while its specific examples make it appropriate for those who teach these fields in developing or emerging countries. New to this edition, in addition to minor revisions of almost all chapters:• Updated data about infectious and non-infectious diseases• An expanded discussion of genetic epidemiology• A new chapter, based on recent research of the authors, on how to build a coherent system of Public Health by using the insights provided by this volume.
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 1493938436 ISBN-13(EAN): 9781493938438 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Описание: This series has been developed specifically for the Cambridge International AS & A Level Mathematics (9709) syllabus to be examined from 2020.
Автор: Gauch, Jr Название: Scientific Method in Brief ISBN: 1107666724 ISBN-13(EAN): 9781107666726 Издательство: Cambridge Academ Рейтинг: Цена: 6494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Specifically designed to enhance perspective and encourage productivity, this is a guide to the key principles of scientific method including deductive and inductive logic, probability, parsimony and hypothesis testing. The examples and case studies span the physical, biological and social sciences and also highlight science`s interrelationship with the humanities.
Автор: David C. M. Dickson, Howard R. Waters, Mary R. Hardy Название: Actuarial mathematics for life contingent risks ISBN: 1108478085 ISBN-13(EAN): 9781108478083 Издательство: Cambridge Academ Рейтинг: Цена: 13147.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This new edition, designed as a primary text for undergraduate and graduate students, covers the mathematics of life and long-term health insurance and pensions. Exam-style questions build up students` confidence in applying the material to real-world situations, and prepares them for professional exams such as the Society of Actuaries` LTAM Exam.
Автор: Thomas A. Garrity Название: All the Math You Missed ISBN: 1009009192 ISBN-13(EAN): 9781009009195 Издательство: Cambridge Academ Рейтинг: Цена: 3960.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The second edition of this bestselling book provides an overview of the key topics in undergraduate mathematics, allowing beginning graduate students to fill in any gaps in their knowledge. With numerous examples, exercises and suggestions for further reading, it is a must-have for anyone looking to learn some serious mathematics quickly.
Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh Название: Introduction to statistical learning ISBN: 1071614177 ISBN-13(EAN): 9781071614174 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Автор: Brunton, Steven L. (university Of Washington) Kutz Название: Data-driven science and engineering ISBN: 1009098489 ISBN-13(EAN): 9781009098489 Издательство: Cambridge Academ Рейтинг: Цена: 7918.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.
Автор: Yuniya Kawamura Название: Doing Research in Fashion and Dress: An Introduction to Qualitative Methods ISBN: 1350089761 ISBN-13(EAN): 9781350089761 Издательство: Bloomsbury Academic Рейтинг: Цена: 8316.00 р. 11880.00-30% Наличие на складе: Есть (1 шт.) Описание: Whether you’re investigating fashion as a material object, an abstract idea, a social phenomenon, or a commercial system, qualitative techniques can further your understanding of almost any research topic. Doing Research in Fashion and Dress begins by guiding you through a brief history of fashion studies, and the debates surrounding it, before introducing key qualitative methodological approaches, including ethnography, semiology, and object-based research. Detailed case studies demonstrate how each methodology is used in practice. These case studies include Japanese subcultures, fashion photography blogs and semiotic studies of fashion magazine shoots and advertisements. This second edition also features a new chapter on internet sources and online ethnography, reflecting the adoption of social media tools not only by industry practitioners but also by academics.By contextualizing history, theory and practice Doing Research in Fashion and Dress offers:-A systematic examination of qualitative research methods in fashion studies in social sciences. -A practical guide for anyone wishing to conduct fashion research in academia or in the business world.-An accessible grounding in contemporary fashion studies literature.
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
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