Bayesian Inference for Hawkes Processes
Jakob Gulddahl Rasmussen ()
Additional contact information
Jakob Gulddahl Rasmussen: Aalborg University
Methodology and Computing in Applied Probability, 2013, vol. 15, issue 3, 623-642
Abstract The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process.
Keywords: Bayesian inference; Cluster process; Hawkes process; Markov chain Monte Carlo; Missing data; Point process; 60G55 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s11009-011-9272-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:15:y:2013:i:3:d:10.1007_s11009-011-9272-5
Ordering information: This journal article can be ordered from
Access Statistics for this article
Methodology and Computing in Applied Probability is currently edited by Joseph Glaz
More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().