Îïèñàíèå: 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.
Îïèñàíèå: There is currently no single book that covers the mathematics, circuits, and electromagnetics backgrounds needed for the study of electromagnetic compatibility (EMC). This book aims to redress the balance by focusing on EMC and providing the background in all three disciplines.
Àâòîð: Alfred Olivier Hero; David Casta??n; Doug Cochran; Íàçâàíèå: Foundations and Applications of Sensor Management ISBN: 1441939113 ISBN-13(EAN): 9781441939111 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 26120.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field.
Àâòîð: Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshn Íàçâàíèå: Secure Networked Inference with Unreliable Data Sources ISBN: 9811323119 ISBN-13(EAN): 9789811323119 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 13974.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
Îïèñàíèå: The integration of logic and probability combines the capability of the first to represent complex relations among entities with the capability of the latter to model uncertainty over attributes and relations. Logic programming provides a Turing complete language based on logic and thus represent an excellent candidate for the integration.Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. One of most successful approaches to Probabilistic Logic Programming is the Distribution Semantics, where a probabilistic logic program defines a probability distribution over normal logic programs and the probability of a ground query is then obtained from the joint distribution of the query and the programs. Foundations of Probabilistic Logic Programming aims at providing an overview of the field of Probabilistic Logic Programming, with a special emphasis on languages under the Distribution Semantics. The book presents the main ideas for semantics, inference and learning and highlights connections between the methods.Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
Àâòîð: Ali H. Sayed Íàçâàíèå: Inference and Learning from Data: Volume 3: Learning ISBN: 100921828X ISBN-13(EAN): 9781009218283 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 12355.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to learning methods. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.
Àâòîð: Ali H. Sayed Íàçâàíèå: Inference and Learning from Data: Volume 2: Inference ISBN: 1009218263 ISBN-13(EAN): 9781009218269 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 12355.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.
Àâòîð: Sayed, Ali H. (ecole Polytechnique Federale De Lausanne) Íàçâàíèå: Inference and learning from data ISBN: 1009218107 ISBN-13(EAN): 9781009218108 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 33264.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: This extraordinary three-volume work provides an accessible, comprehensive introduction to mathematical and statistical techniques for data-driven learning and inference. Ideal for early-career researchers and graduate students across signal processing, machine learning, statistics and data science.
Îïèñàíèå: ‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models.From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks.In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
Àâòîð: Cuppens Íàçâàíèå: Foundations and Practice of Security ISBN: 3319519654 ISBN-13(EAN): 9783319519654 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 9083.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå:
This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Symposium on Foundations and Practice of Security, FPS 2016, held in Qu bec City, QC, Canada, in October 2016.
The 18 revised regular papers presented together with 5 short papers and 3 invited talks were carefully reviewed and selected from 34 submissions.
The accepted papers cover diverse research themes, ranging from classic topics, such as malware, anomaly detection, and privacy, to emerging issues, such as security and privacy in mobile computing and cloud.
Àâòîð: Peters Jonas, Janzing Dominik, Scholkopf Bernhard Íàçâàíèå: Elements of Causal Inference: Foundations and Learning Algorithms ISBN: 0262037319 ISBN-13(EAN): 9780262037310 Èçäàòåëüñòâî: MIT Press Ðåéòèíã: Öåíà: 7719.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå:
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.
The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.
After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.
The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Àâòîð: Blum Avrim Íàçâàíèå: Foundations of Data Science ISBN: 1108485065 ISBN-13(EAN): 9781108485067 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 7445.00 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.
Îïèñàíèå: This book is aimed towards both undergraduate and graduate courses in computer science on the design and analysis of algorithms for data. The material in this book will provide students with the mathematical background they need for further study and research in machine learning, data mining, and data science more generally.