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  1. Methodology and Computing in Applied Probability
  2. Methodology and Computing in Applied Probability : Volume 15
  3. Methodology and Computing in Applied Probability : Volume 15, Issue 3, September 2013
  4. Bayesian Inference for Hawkes Processes
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Methodology and Computing in Applied Probability : Volume 19
Methodology and Computing in Applied Probability : Volume 18
Methodology and Computing in Applied Probability : Volume 17
Methodology and Computing in Applied Probability : Volume 16
Methodology and Computing in Applied Probability : Volume 15
Methodology and Computing in Applied Probability : Volume 15, Issue 4, December 2013
Methodology and Computing in Applied Probability : Volume 15, Issue 3, September 2013
Exact Computation and Approximation of Stochastic and Analytic Parameters of Generalized Sierpinski Gaskets
Monte Carlo Computation of the Laplace Transform of Exponential Brownian Functionals
Expansions for the Distribution of the Maximum from Distributions with a Power Tail when a Trend is Present
Multi-asset American Options and Parallel Quantization
A Simple and Complete Computational Analysis of MAP/R/1 Queue Using Roots
Runs Based on Records: Their Distributional Properties and an Application to Testing for Dispersive Ordering
The Single Server Queue with Catastrophes and Geometric Reneging
Bayesian Inference for Hawkes Processes
On the Arrangement of Cells in Planar STIT and Poisson Line Tessellations
Tail Behavior of Poisson Shot Noise Processes under Heavy-tailed Shocks and Actuarial Applications
An Urn Model for Population Mixing and the Phases Within
Appendix to “Approximating Perpetuities”
Random Motion on Simple Graphs: Addendum
Methodology and Computing in Applied Probability : Volume 15, Issue 2, June 2013
Methodology and Computing in Applied Probability : Volume 15, Issue 1, March 2013
Methodology and Computing in Applied Probability : Volume 14
Methodology and Computing in Applied Probability : Volume 13
Methodology and Computing in Applied Probability : Volume 12
Methodology and Computing in Applied Probability : Volume 11
Methodology and Computing in Applied Probability : Volume 10
Methodology and Computing in Applied Probability : Volume 9
Methodology and Computing in Applied Probability : Volume 8
Methodology and Computing in Applied Probability : Volume 7
Methodology and Computing in Applied Probability : Volume 6
Methodology and Computing in Applied Probability : Volume 5
Methodology and Computing in Applied Probability : Volume 4
Methodology and Computing in Applied Probability : Volume 3
Methodology and Computing in Applied Probability : Volume 2
Methodology and Computing in Applied Probability : Volume 1

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Bayesian Inference for Hawkes Processes

Content Provider SpringerLink
Author Rasmussen, Jakob Gulddahl
Copyright Year 2011
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.
Starting Page 623
Ending Page 642
Page Count 20
File Format PDF
ISSN 13875841
Journal Methodology and Computing in Applied Probability
Volume Number 15
Issue Number 3
e-ISSN 15737713
Language English
Publisher Springer US
Publisher Date 2011-12-20
Publisher Place Boston
Access Restriction Subscribed
Subject Keyword Bayesian inference Cluster process Hawkes process Markov chain Monte Carlo Missing data Point process Statistics Life Sciences Electrical Engineering Economics general Business/Management Science
Content Type Text
Resource Type Article
Subject Statistics and Probability
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