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Probability Methods for Cost Uncertainty Analysis, 


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Название:  Probability Methods for Cost Uncertainty Analysis
ISBN: 9781482219753
Издательство: Taylor&Francis
Классификация:


ISBN-10: 1482219751
Обложка/Формат: Hardback
Страницы: 528
Вес: 0.94 кг.
Дата издания: 22.12.2015
Язык: English
Издание: 2 ed
Иллюстрации: 62 tables, black and white; 171 illustrations, black and white
Размер: 160 x 236 x 38
Подзаголовок: A systems engineering perspective, second edition
Ссылка на Издательство: Link
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Поставляется из: Европейский союз


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Dicing with death

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Описание: From measles to malaria, from clinical trials to COVID and from life tables to the law, the second edition of Dicing with Death explains how the vital decisions we have to make both individually and collectively can be informed and improved by good data, statistical reasoning and analysis.

All the Math You Missed

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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.

Mathematics for Machine Learning

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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.

Analysis of Queues

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Название: Analysis of Queues
ISBN: 1439806586 ISBN-13(EAN): 9781439806586
Издательство: Taylor&Francis
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Written with students and professors in mind, Analysis of Queues: Methods and Applications combines coverage of classical queueing theory with recent advances in studying stochastic networks. Exploring a broad range of applications, the book contains plenty of solved problems, exercises, case studies, paradoxes, and numerical examples.

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This book is about a recently developed class of strategies, known as the fence methods, which fits particularly well in non-conventional and complex model selection problems with practical considerations. The idea involves a procedure to isolate a subgroup of what are known as correct models, of which the optimal model is a member. This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from amongst those within the fence according to a criterion which can be made flexible. In particular, the criterion of optimality can incorporate consideration of practical interest, thus making model selection a real life practice.

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