The Elements of Hawkes Processes

• Verfasst von: Laub, Patrick J. [VerfasserIn]
• Verfasst von: Lee, Young [VerfasserIn]
• Verfasst von: Taimre, Thomas [VerfasserIn]
• Titel: The Elements of Hawkes Processes
• Verf.angabe: by Patrick J. Laub, Young Lee, Thomas Taimre
• Ausgabe: 1st ed. 2021.
• Verlagsort: Cham
• Verlagsort: Cham
• Verlag: Springer International Publishing
• Verlag: Imprint: Springer
• E-Jahr: 2021
• Jahr: 2021.
• Jahr: 2021.
• Umfang: 1 Online-Ressource(XIV, 133 p. 121 illus., 119 illus. in color.)
• Gesamttitel/Reihe: Springer eBook Collection
• ISBN: 978-3-030-84639-8
• DOI: doi:10.1007/978-3-030-84639-8
• Schlagwörter: (s)Stochastischer Prozess
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Laub, Patrick J.: The elements of Hawkes processes. - 1st edition. - Cham : Springer International Publishing, 2021. - xiv,133 Seiten
• RVK-Notation: SK 820
• K10plus-PPN: 1785427741
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

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.

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.