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:
    non sort: The
    title: Elements of Hawkes Processes
  name:
    name part: Laub, Patrick J.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Lee, Young.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Taimre, Thomas.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2021
    edition: 1st ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XIV, 133 pages 121 illustrations, 119 illustrations in color)
  abstract: Hawkes processes are studied and used in a wide range of disciplines: mathematics, social sciences, and earthquake modelling, to name a few. This book presents a selective coverage of the core and recent topics in the broad field of Hawkes processes. It consists of three parts. Parts I and II summarise and provide an overview of core theory (including key simulation methods) and inference methods, complemented by a selection of recent research developments and applications. Part III is devoted to case studies in seismology and finance that connect the core theory and inference methods to practical scenarios. This book is designed primarily for applied probabilists, statisticians, and machine learners. However, the mathematical prerequisites have been kept to a minimum so that the content will also be of interest to undergraduates in advanced mathematics and statistics, as well as machine learning practitioners. Knowledge of matrix theory with basics of probability theory, including Poisson processes, is considered a prerequisite. Colour-blind-friendly illustrations are included.
  table of contents: Background -- Hawes Process Essentials -- Simulation Methods -- Likelihood Methods -- EM Algorithm -- Bayesian Methods -- Spectral Methods -- Goodness of Fit -- Traditional Applications -- Financial and Actuarial Applications -- Biological Applications.
  note: by Patrick J. Laub, Young Lee, Thomas Taimre.
  subject:
    topic: Statistics
  subject:
    topic: Probabilities
  subject:
    topic: Machine learning
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Probability Theory
  subject:
    topic: Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Machine Learning
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030846398
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 220103
    record change date: 20230330185037.5
    record identifier: in00004556702