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The Elements of Hawkes Processes

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The Elements of Hawkes Processes/ by Patrick J. Laub, Young Lee, Thomas Taimre.
作者:	Laub, Patrick J.
其他作者:	Taimre, Thomas.
面頁冊數:	XIV, 133 p. 121 illus., 119 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-84639-8
ISBN:	9783030846398

The Elements of Hawkes Processes
Laub, Patrick J.

The Elements of Hawkes Processes[electronic resource] /by Patrick J. Laub, Young Lee, Thomas Taimre. - 1st ed. 2021. - XIV, 133 p. 121 illus., 119 illus. in color.online resource.

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.

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.

ISBN: 9783030846398

Standard No.: 10.1007/978-3-030-84639-8doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

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