Spectral Methods for Data Science: A Statistical Perspective, Cong Ma, Jianqing Fan, Yuejie Chi, Yuxin Chen

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: 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: 33790.00 T
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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: Abarbanel, Henry D. I. (university Of California, San Diego)
Название: Statistical physics of data assimilation and machine learning
ISBN: 1316519635 ISBN-13(EAN): 9781316519639
Издательство: Cambridge Academ
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Цена: 58070.00 T
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Описание: The theory of data assimilation and machine learning is introduced in an accessible and pedagogical manner, with a focus on the underlying statistical physics. This modern and cross-disciplinary book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Автор: Ery Arias-Castro
Название: Principles of Statistical Analysis: Learning from Randomized Experiments
ISBN: 1108489672 ISBN-13(EAN): 9781108489676
Издательство: Cambridge Academ
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Цена: 87650.00 T
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Описание: This concise course in principled data analysis for the mathematically literate uses survey sampling and designed experiments as a foundation for statistical inference. Covering essentials for advanced undergraduates and selected topics typically taught at the graduate level, its 700 problems - many computational - build understanding and skills.

Автор: Balzano Simona, Porzio Giovanni C., Salvatore Renato
Название: Statistical Learning and Modeling in Data Analysis: Methods and Applications
ISBN: 3030699439 ISBN-13(EAN): 9783030699437
Издательство: Springer
Цена: 149060.00 T
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Описание: The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications.

Автор: Ery Arias-Castro
Название: Principles of Statistical Analysis: Learning from Randomized Experiments
ISBN: 1108747442 ISBN-13(EAN): 9781108747448
Издательство: Cambridge Academ
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Цена: 32730.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This concise course in principled data analysis for the mathematically literate uses survey sampling and designed experiments as a foundation for statistical inference. Covering essentials for advanced undergraduates and selected topics typically taught at the graduate level, its 700 problems - many computational - build understanding and skills.

Автор: Agarwal
Название: Statistical Methods for Recommender Systems
ISBN: 1107036070 ISBN-13(EAN): 9781107036079
Издательство: Cambridge Academ
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Цена: 50680.00 T
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Описание: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

Автор: Nagahara Masaaki
Название: Sparsity Methods for Systems and Control
ISBN: 1680837249 ISBN-13(EAN): 9781680837247
Издательство: Mare Nostrum (Eurospan)
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Цена: 82230.00 T
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Описание: Offers a comprehensive guide to sparsity methods for systems and control, from standard sparsity methods in finite-dimensional vector spaces to optimal control methods in infinite-dimensional function spaces.The primary objective of this book is to show how to use sparsity methods for several engineering problems.

Автор: Montesinos Lуpez Osval Antonio, Montesinos Lуpez Abelardo, Crossa Josй
Название: Multivariate Statistical Machine Learning Methods for Genomic Prediction
ISBN: 3030890090 ISBN-13(EAN): 9783030890094
Издательство: Springer
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Цена: 37260.00 T
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Описание: It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool.

Автор: Wainwright Martin J
Название: Cambridge Series in Statistical and Probabilistic Mathematic
ISBN: 1108498027 ISBN-13(EAN): 9781108498029
Издательство: Cambridge Academ
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Цена: 71810.00 T
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Описание: Recent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.

Автор: Bouveyron, Charles Celeux, Gilles Murphy, T. Brendan (university College Dublin) Raftery, Adrian E. (university Of Washington)
Название: Cambridge series in statistical and probabilistic mathematics
ISBN: 110849420X ISBN-13(EAN): 9781108494205
Издательство: Cambridge Academ
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Цена: 77090.00 T
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Описание: This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.

Автор: Concha Bielza, Pedro Larranaga
Название: Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
ISBN: 110849370X ISBN-13(EAN): 9781108493703
Издательство: Cambridge Academ
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Цена: 85530.00 T
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Описание: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods.