Journal
METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY
Volume 15, Issue 3, Pages 623-642Publisher
SPRINGER
DOI: 10.1007/s11009-011-9272-5
Keywords
Bayesian inference; Cluster process; Hawkes process; Markov chain Monte Carlo; Missing data; Point process
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Funding
- Danish Natural Science Research Council [09-072331]
- Centre for Stochastic Geometry and Advanced Bioimaging
- Villum Foundation
- Villum Fonden [00008721] Funding Source: researchfish
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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.
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