Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling, Thulin, M?ns

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: 10480.00 р.
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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: Brunton, Steven L. (university Of Washington) Kutz
Название: Data-driven science and engineering
ISBN: 1009098489 ISBN-13(EAN): 9781009098489
Издательство: Cambridge Academ
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Цена: 7918.00 р.
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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: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 5069.00 р.
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Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Автор: Thulin, M?ns
Название: Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling
ISBN: 1032497459 ISBN-13(EAN): 9781032497457
Издательство: Taylor&Francis
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Цена: 25265.00 р.
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Автор: Bradley Boehmke
Название: Data Wrangling with R
ISBN: 3319455982 ISBN-13(EAN): 9783319455983
Издательство: Springer
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Цена: 9781.00 р.
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Описание: This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and datesThe difference between different data structures and how to create, add additional components to, and subset each data structureHow to acquire and parse data from locations previously inaccessibleHow to develop functions and use loop control structures to reduce code redundancyHow to use pipe operators to simplify code and make it more readableHow to reshape the layout of data and manipulate, summarize, and join data sets

Автор: Clarke, Bertrand S. (university Of Nebraska, Lincoln) Clarke, Jennifer L. (university Of Nebraska, Lincoln)
Название: Predictive statistics
ISBN: 1107028280 ISBN-13(EAN): 9781107028289
Издательство: Cambridge Academ
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Цена: 12514.00 р.
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Описание: Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical problems outlined embraces traditional subfields and `black box` settings, with computed examples.

Автор: Gustafson, Paul
Название: Bayesian Inference for Partially Identified Models
ISBN: 036757053X ISBN-13(EAN): 9780367570538
Издательство: Taylor&Francis
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Цена: 7961.00 р.
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Автор: Mavrakakis
Название: Modelling, Inference and Data Analysis
ISBN: 158488939X ISBN-13(EAN): 9781584889397
Издательство: Taylor&Francis
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Цена: 19906.00 р.
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Описание: Covers aspects of probability, distribution theory and random processes that are fundamental to a proper understanding of inference. This book discusses the properties of estimators constructed from a random sample of ends, with sections on methods for estimating parameters in time series models.

Автор: Geisser, Seymour
Название: Predictive Inference
ISBN: 0367449919 ISBN-13(EAN): 9780367449919
Издательство: Taylor&Francis
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Цена: 9492.00 р.
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Описание: This book presents Seymour Geisser`s views on predictive or observable inference and its advantages over parametric inference. It focuses on the predictive applications of the Bayesian approach. The book also presents predictive analyses that have no real parametric analogues.

Автор: Geisser, Seymour
Название: Predictive Inference
ISBN: 0412034719 ISBN-13(EAN): 9780412034718
Издательство: Taylor&Francis
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Цена: 27562.00 р.
Наличие на складе: Нет в наличии.

Автор: Pawitan, Yudi (professor Of Statistics, Department Of Statistics, National University Of Ireland, Cork)
Название: In all likelihood
ISBN: 0199671222 ISBN-13(EAN): 9780199671229
Издательство: Oxford Academ
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Цена: 9979.00 р.
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Автор: Pavel V. Shevchenko
Название: Modelling Operational Risk Using Bayesian Inference
ISBN: 3642423531 ISBN-13(EAN): 9783642423536
Издательство: Springer
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Цена: 16769.00 р.
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Описание: This has formally defined operational risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses.