rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Probabilistic Foundations of Statistical Network Analysis
  title info:
    title: MATHnetBASE
  name:
    name part: Crane, Harry
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2018
    edition: First edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xx, 236 pages)
  abstract: "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 authors 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 Cranes research intere…
  table of contents: chapter 1 Orientation / Harry Crane -- chapter 2 Binary relational data / Harry Crane -- chapter 3 Network sampling / Harry Crane -- chapter 4 Generative models / Harry Crane -- chapter 5 Statistical modeling paradigm / Harry Crane -- chapter 6 Vertex exchangeable models / Harry Crane -- chapter 7 Getting beyond graphons / Harry Crane -- chapter 8 Relatively exchangeable models / Harry Crane -- chapter 9 Edge exchangeable models / Harry Crane -- chapter 10 Relationally exchangeable models / Harry Crane -- chapter 11 Dynamic network models / Harry Crane.
  note: Harry Crane (Rutgers University, New Jersey, USA).
  subject:
    topic: Computational linguistics
    topic: Network analysis
  subject:
    topic: Machine learning
  subject:
    topic: Operations research
  classification: P98.5.N48 C73 2018
  classification: 410.285 C891
  related item:
    title info:
      title: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
  identifier: 9781315209661
  identifier: 9781351807319
  identifier: 9781138585997
  identifier: 9781138630154
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: FlBoTFG
    record creation date: 180706
    record change date: 20220613175626.1
    record identifier: in00004580463