- We will send in 10–14 business days.
/images/books/5647270/9781108498029.jpg)
- Author: Martin J Wainwright
- Publisher: Cambridge University Press
- ISBN-10: 1108498027
- ISBN-13: 9781108498029
- Format: 18.5 x 25.7 x 4.3 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 23d.08:51:30
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Martin J Wainwright
- Publisher: Cambridge University Press
- ISBN-10: 1108498027
- ISBN-13: 9781108498029
- Format: 18.5 x 25.7 x 4.3 cm, kieti viršeliai
- Language: English English
Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.
Reviews