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Probabilistic foundations of statist...

Crane, Harry ((Statistics professor),)

Probabilistic foundations of statistical network analysis /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Probabilistic foundations of statistical network analysis /Harry Crane.
作者:	Crane, Harry
出版者:	Boca Raton, FL :CRC Press, Taylor & Francis Group,c2018.
面頁冊數:	xx, 236 p. :ill. ;24 cm.
標題:	Network analysis (Planning)
ISBN:	9781138585997 :

Probabilistic foundations of statistical network analysis /
Crane, Harry(Statistics professor),

Probabilistic foundations of statistical network analysis /Harry Crane. - Boca Raton, FL :CRC Press, Taylor & Francis Group,c2018. - xx, 236 p. :ill. ;24 cm. - Monographs on statistics and applied probability ;157 . - Monographs on statistics and applied probability (Series) ;119..

Includes bibliographical references and index.

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchange ability, relative exchange ability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author's incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics.--

ISBN: 9781138585997 :GBP$100

LCCN: 2018-006902 Subjects--Topical Terms:

829184
Network analysis (Planning)

LC Class. No.: QA402 / C891 2018

Probabilistic foundations of statistical network analysis /
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490 1 $a Monographs on statistics and applied probability ; $v 157
504 $a Includes bibliographical references and index.
520 $a Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchange ability, relative exchange ability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author's incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics.-- $c Provided by Publisher.
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