Probabilistic Foundations Of Statistical Network Analysis

Download Probabilistic Foundations Of Statistical Network Analysis full books in PDF, epub, and Kindle. Read online Probabilistic Foundations Of Statistical Network Analysis ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Probabilistic Foundations of Statistical Network Analysis

Author : Harry Crane
Publisher : CRC Press
ISBN 13 : 1351807323
Total Pages : 432 pages
Book Rating : 4.3/5 (518 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Foundations of Statistical Network Analysis by : Harry Crane

Download or read book Probabilistic Foundations of Statistical Network Analysis written by Harry Crane and published by CRC Press. This book was released on 2018-04-17 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 exchangeability, relative exchangeability, 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. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Probabilistic Foundations of Statistical Network Analysis

Author : Harry Crane
Publisher : CRC Press
ISBN 13 : 1351807331
Total Pages : 236 pages
Book Rating : 4.3/5 (518 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Foundations of Statistical Network Analysis by : Harry Crane

Download or read book Probabilistic Foundations of Statistical Network Analysis written by Harry Crane and published by CRC Press. This book was released on 2018-04-17 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 exchangeability, relative exchangeability, 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. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Statistical Network Analysis: Models, Issues, and New Directions

Author : Edoardo M. Airoldi
Publisher : Springer
ISBN 13 : 3540731334
Total Pages : 200 pages
Book Rating : 4.5/5 (47 download)

DOWNLOAD NOW!

Book Synopsis Statistical Network Analysis: Models, Issues, and New Directions by : Edoardo M. Airoldi

Download or read book Statistical Network Analysis: Models, Issues, and New Directions written by Edoardo M. Airoldi and published by Springer. This book was released on 2008-04-12 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.

Topics at the Frontier of Statistics and Network Analysis

Author : Eric D. Kolaczyk
Publisher : Cambridge University Press
ISBN 13 : 110830561X
Total Pages : 214 pages
Book Rating : 4.1/5 (83 download)

DOWNLOAD NOW!

Book Synopsis Topics at the Frontier of Statistics and Network Analysis by : Eric D. Kolaczyk

Download or read book Topics at the Frontier of Statistics and Network Analysis written by Eric D. Kolaczyk and published by Cambridge University Press. This book was released on 2017-08-10 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done.

A Survey of Statistical Network Models

Author : Anna Goldenberg
Publisher : Now Publishers Inc
ISBN 13 : 1601983204
Total Pages : 118 pages
Book Rating : 4.6/5 (19 download)

DOWNLOAD NOW!

Book Synopsis A Survey of Statistical Network Models by : Anna Goldenberg

Download or read book A Survey of Statistical Network Models written by Anna Goldenberg and published by Now Publishers Inc. This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.

Statistical Analysis of Network Data

Author : Eric D. Kolaczyk
Publisher : Springer Science & Business Media
ISBN 13 : 0387881468
Total Pages : 397 pages
Book Rating : 4.3/5 (878 download)

DOWNLOAD NOW!

Book Synopsis Statistical Analysis of Network Data by : Eric D. Kolaczyk

Download or read book Statistical Analysis of Network Data written by Eric D. Kolaczyk and published by Springer Science & Business Media. This book was released on 2009-04-20 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years there has been an explosion of network data – that is, measu- ments that are either of or from a system conceptualized as a network – from se- ingly all corners of science. The combination of an increasingly pervasive interest in scienti c analysis at a systems level and the ever-growing capabilities for hi- throughput data collection in various elds has fueled this trend. Researchers from biology and bioinformatics to physics, from computer science to the information sciences, and from economics to sociology are more and more engaged in the c- lection and statistical analysis of data from a network-centric perspective. Accordingly, the contributions to statistical methods and modeling in this area have come from a similarly broad spectrum of areas, often independently of each other. Many books already have been written addressing network data and network problems in speci c individual disciplines. However, there is at present no single book that provides a modern treatment of a core body of knowledge for statistical analysis of network data that cuts across the various disciplines and is organized rather according to a statistical taxonomy of tasks and techniques. This book seeks to ll that gap and, as such, it aims to contribute to a growing trend in recent years to facilitate the exchange of knowledge across the pre-existing boundaries between those disciplines that play a role in what is coming to be called ‘network science.

Networks and Chaos - Statistical and Probabilistic Aspects

Author : J L Jensen
Publisher : CRC Press
ISBN 13 : 9780412465307
Total Pages : 324 pages
Book Rating : 4.4/5 (653 download)

DOWNLOAD NOW!

Book Synopsis Networks and Chaos - Statistical and Probabilistic Aspects by : J L Jensen

Download or read book Networks and Chaos - Statistical and Probabilistic Aspects written by J L Jensen and published by CRC Press. This book was released on 1993-07-22 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume consists of a collection of tutorial papers by leading experts on statistical and probabilistic aspects of chaos and networks, in particular neural networks. While written for the non-expert, they are intended to bring the reader up to the forefront of knowledge and research in the subject areas concerned. The papers, which contain extensive references to the literature, can separately or in various combinations serve as bases for short- or full-length courses, at graduate or more advanced levels. The papers are directed not only to mathematical statisticians but also to students and researchers in related fields of biology, engineering, geology, physics and probability.

Probabilistic Inference and Statistical Methods in Network Analysis

Author : Olga Moreira
Publisher : Arcler Press
ISBN 13 : 9781773615554
Total Pages : 0 pages
Book Rating : 4.6/5 (155 download)

DOWNLOAD NOW!

Book Synopsis Probabilistic Inference and Statistical Methods in Network Analysis by : Olga Moreira

Download or read book Probabilistic Inference and Statistical Methods in Network Analysis written by Olga Moreira and published by Arcler Press. This book was released on 2018-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book, Probabilistic Inference and Statistical Methods in Network Analysis, is a collection of contemporary open access articles which highlight the development of computational methods for constructing social and biological networks; detecting the topological structure of a network and identifying important nodes within. This book features two classes of computational methods currently used in network analysis: (a) model-free methods based on statistical and information theory measures such as centrality, correlation, cross-correlation, and partial-correlation, mutual information, joint entropy, and transfer entropy; and (b) generative model-based methods. The intended audience of this edited book will mainly consist of researchers and graduate students in the Natural and Computer Sciences. The book is also of particular interest to scientists and engineers in areas such as machine learning, data mining, information theory computational neuroscience, and biological systems. It is suitable for readers with basic knowledge of statistical inference, differential equations, calculus, algebra, graph theory scientific modelling and computer simulation. Book jacket.

Statistical Analysis of Network Data with R

Author : Eric D. Kolaczyk
Publisher : Springer
ISBN 13 : 1493909835
Total Pages : 207 pages
Book Rating : 4.4/5 (939 download)

DOWNLOAD NOW!

Book Synopsis Statistical Analysis of Network Data with R by : Eric D. Kolaczyk

Download or read book Statistical Analysis of Network Data with R written by Eric D. Kolaczyk and published by Springer. This book was released on 2014-05-22 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Quantitative Analysis of Ecological Networks

Author : Mark R. T. Dale
Publisher : Cambridge University Press
ISBN 13 : 1108632971
Total Pages : 250 pages
Book Rating : 4.1/5 (86 download)

DOWNLOAD NOW!

Book Synopsis Quantitative Analysis of Ecological Networks by : Mark R. T. Dale

Download or read book Quantitative Analysis of Ecological Networks written by Mark R. T. Dale and published by Cambridge University Press. This book was released on 2021-04-15 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Network thinking and network analysis are rapidly expanding features of ecological research. Network analysis of ecological systems include representations and modelling of the interactions in an ecosystem, in which species or factors are joined by pairwise connections. This book provides an overview of ecological network analysis including generating processes, the relationship between structure and dynamic function, and statistics and models for these networks. Starting with a general introduction to the composition of networks and their characteristics, it includes details on such topics as measures of network complexity, applications of spectral graph theory, how best to include indirect species interactions, and multilayer, multiplex and multilevel networks. Graduate students and researchers who want to develop and understand ecological networks in their research will find this volume inspiring and helpful. Detailed guidance to those already working in network ecology but looking for advice is also included.

Handbook of Econometrics

Author :
Publisher : Elsevier
ISBN 13 : 0444636544
Total Pages : 594 pages
Book Rating : 4.4/5 (446 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Econometrics by :

Download or read book Handbook of Econometrics written by and published by Elsevier. This book was released on 2020-11-25 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries. Presents a broader and more comprehensive view of this expanding field than any other handbook Emphasizes the connection between econometrics and economics Highlights current topics for which no good summaries exist

The Statistical Analysis of Multivariate Failure Time Data

Author : Ross L. Prentice
Publisher : CRC Press
ISBN 13 : 0429529708
Total Pages : 110 pages
Book Rating : 4.4/5 (295 download)

DOWNLOAD NOW!

Book Synopsis The Statistical Analysis of Multivariate Failure Time Data by : Ross L. Prentice

Download or read book The Statistical Analysis of Multivariate Failure Time Data written by Ross L. Prentice and published by CRC Press. This book was released on 2019-05-14 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

Complex Networks and Their Applications VIII

Author : Hocine Cherifi
Publisher : Springer Nature
ISBN 13 : 3030366839
Total Pages : 1047 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

Book Synopsis Complex Networks and Their Applications VIII by : Hocine Cherifi

Download or read book Complex Networks and Their Applications VIII written by Hocine Cherifi and published by Springer Nature. This book was released on 2019-11-26 with total page 1047 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Multistate Models for the Analysis of Life History Data

Author : Richard J Cook
Publisher : CRC Press
ISBN 13 : 1498715613
Total Pages : 440 pages
Book Rating : 4.4/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Multistate Models for the Analysis of Life History Data by : Richard J Cook

Download or read book Multistate Models for the Analysis of Life History Data written by Richard J Cook and published by CRC Press. This book was released on 2018-05-15 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks and progressive processes are considered, as well as more complex models. The book provides both theoretical development and illustrations of analysis based on data from randomized trials and observational cohort studies in health research. It features: Discusses a wide range of applications of multistate models, Presents methods for both continuously and intermittently observed life history processes, Gives a thorough discussion of conditionally independent censoring and observation processes, Discusses models with random effects and joint models for two or more multistate processes, Discusses and illustrates software for multistate analysis that is available in R, Target audience includes those engaged in research and applications involving multistate models.

Nonparametric Models for Longitudinal Data

Author : Colin O. Wu
Publisher : CRC Press
ISBN 13 : 0429939086
Total Pages : 552 pages
Book Rating : 4.4/5 (299 download)

DOWNLOAD NOW!

Book Synopsis Nonparametric Models for Longitudinal Data by : Colin O. Wu

Download or read book Nonparametric Models for Longitudinal Data written by Colin O. Wu and published by CRC Press. This book was released on 2018-05-23 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features: Provides an overview of parametric and semiparametric methods Shows smoothing methods for unstructured nonparametric models Covers structured nonparametric models with time-varying coefficients Discusses nonparametric shared-parameter and mixed-effects models Presents nonparametric models for conditional distributions and functionals Illustrates implementations using R software packages Includes datasets and code in the authors’ website Contains asymptotic results and theoretical derivations

Sequential Change Detection and Hypothesis Testing

Author : Alexander Tartakovsky
Publisher : CRC Press
ISBN 13 : 1498757596
Total Pages : 321 pages
Book Rating : 4.4/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Sequential Change Detection and Hypothesis Testing by : Alexander Tartakovsky

Download or read book Sequential Change Detection and Hypothesis Testing written by Alexander Tartakovsky and published by CRC Press. This book was released on 2019-12-11 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods for sequential hypothesis testing and changepoint detection have applications across many fields, including quality control, biomedical engineering, communication networks, econometrics, image processing, security, etc. This book presents an overview of methodology in these related areas, providing a synthesis of research from the last few decades. The methods are illustrated through real data examples, and software is referenced where possible. The emphasis is on providing all the theoretical details in a unified framework, with pointers to new research directions.

Multivariate Kernel Smoothing and Its Applications

Author : José E. Chacón
Publisher : CRC Press
ISBN 13 : 0429939140
Total Pages : 226 pages
Book Rating : 4.4/5 (299 download)

DOWNLOAD NOW!

Book Synopsis Multivariate Kernel Smoothing and Its Applications by : José E. Chacón

Download or read book Multivariate Kernel Smoothing and Its Applications written by José E. Chacón and published by CRC Press. This book was released on 2018-05-08 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges. Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both aspects. It begins with a thorough exposition of the approaches to achieve the two basic goals of estimating probability density functions and their derivatives. The focus then turns to the applications of these approaches to more complex data analysis goals, many with a geometric/topological flavour, such as level set estimation, clustering (unsupervised learning), principal curves, and feature significance. Other topics, while not direct applications of density (derivative) estimation but sharing many commonalities with the previous settings, include classification (supervised learning), nearest neighbour estimation, and deconvolution for data observed with error. For a data scientist, each chapter contains illustrative Open data examples that are analysed by the most appropriate kernel smoothing method. The emphasis is always placed on an intuitive understanding of the data provided by the accompanying statistical visualisations. For a reader wishing to investigate further the details of their underlying statistical reasoning, a graduated exposition to a unified theoretical framework is provided. The algorithms for efficient software implementation are also discussed. José E. Chacón is an associate professor at the Department of Mathematics of the Universidad de Extremadura in Spain. Tarn Duong is a Senior Data Scientist for a start-up which provides short distance carpooling services in France. Both authors have made important contributions to kernel smoothing research over the last couple of decades.