Introduction to statistical learning, James, Gareth Witten, Daniela Hastie, Trevor Tibshirani, Robert

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
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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.

Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh
Название: Introduction to statistical learning
ISBN: 1071614177 ISBN-13(EAN): 9781071614174
Издательство: Springer
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Цена: 55890.00 T
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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: Chandrupatla, Tirupathi, Belegundu, Ashok
Название: Introduction to finite elements in engineering
ISBN: 1108841414 ISBN-13(EAN): 9781108841412
Издательство: Cambridge Academ
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Цена: 79190.00 T
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Описание: Thoroughly updated with improved pedagogy, the fifth edition provides senior undergraduate and graduate students with a clear, comprehensive introduction to the field. Features enhanced coverage of introductory topics, over thirty additional solved problems; downloadable Matlab, Python, C, and Javascript code; and solutions for instructors.

Автор: Efron
Название: An Introduction to the Bootstrap
ISBN: 0412042312 ISBN-13(EAN): 9780412042317
Издательство: Taylor&Francis
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Цена: 153120.00 T
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Описание: An exploration of the many different bootstrap techniques. It discusses useful statistical techniques through real data examples and covers nonparametric regression, density estimation, classification trees, and least median squares regression. There are numerous exercises.

Автор: Bacci Silvia, Chiandotto Bruno
Название: Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis
ISBN: 1032091754 ISBN-13(EAN): 9781032091754
Издательство: Taylor&Francis
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Цена: 50010.00 T
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Описание: This book provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference.

Автор: Ross, Sheldon M.
Название: Introduction To Probability And Statistics For Engineers And Scientists
ISBN: 0128243465 ISBN-13(EAN): 9780128243466
Издательство: Elsevier Science
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Цена: 110030.00 T
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Описание: Letter Jam is a 2-6 player cooperative word game where players assist each other in composing meaningful words from letters around the table. The trick is holding the letter card so that it`s only visible to other players and not to you.At the start of the game, each player receives a set of face-down letter cards that can be arranged to form an existing word. The setup can be prepared by using a special card scanning app, or by players selecting words for each other. Each player then puts their first card in their stand facing the other players without looking at it, and the game begins.The game is played in turns. Each turn, players simultaneously search other players` letters to see what words they can spell out (telling the others the length of the word they can make up). The player who offers the longest word can then be chosen as the clue giver.The clue giver spells out their clue by putting numbered tokens in front of the other players. Number one goes to the player whose letter comes first in the clue, number two to the second letter etc. They can always use a wild card which can be any letter, but they cannot tell others which letter it represents.Each player with a numbered token (or tokens) in front of them then tries to figure out what their letter is. If they do, they place the card face down before revealing the next letter. At the end of the game, players can then rearrange the cards to try to form an existing word. All players then reveal their cards to see if they were successful or not. The more players who have an existing word in front of them, the bigger their common success.

Автор: Little
Название: Statistical Analysis with Missing Data, Third Edit ion
ISBN: 0470526793 ISBN-13(EAN): 9780470526798
Издательство: Wiley
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Цена: 84430.00 T
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Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.