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BEGIN:VEVENT
DTSTAMP:20260406T211515Z
DTSTART;VALUE=DATE-TIME:20221206T170000
DTEND;VALUE=DATE-TIME:20221206T180000
SUMMARY:Raiha Browning on Bayesian nonparametric approach for discrete-ti
 me Hawkes processes
TZID:Europe/London
UID:20221206-8a1785d883a32c410183a354a8d80159@warwick.ac.uk
CREATED:20221206T140815Z
DESCRIPTION:Flexible estimation of discrete-time Hawkes processes Hawkes 
 processes are a self-exciting stochastic process to describe phenomena f
 or which past events increase the probability of future events occurring
 . Most standard models of Hawkes processes rely on a parametric form for
  the self-exciting term of the intensity function\, referred to as the t
 riggering kernel\, which describes the influence of past events. This is
  insufficient to capture the true excitation pattern for complex data. T
 his work presents a flexible approach for modelling a discrete-time vari
 ant of the traditional Hawkes process. We develop a Bayesian nonparametr
 ic approach based on random histogram priors to model the triggering ker
 nel for discrete-time Hawkes processes\, allowing for significantly more
  flexibility than a parametric form. To estimate the histogram function\
 , a clustering algorithm based on the Chinese restaurant process is prop
 osed. The proposed model exploits the branching structure of the Hawkes 
 process\, which describes the parent and offspring relationship between 
 the events in the process through time.
LOCATION:
CATEGORIES:
LAST-MODIFIED:20221206T140815Z
ORGANIZER;CN=Claudia Viaro:
END:VEVENT
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