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NONPARAMETRIC BAYESIAN ESTIMATION OF MULTIVARIATE HAWKES PROCESSES

Authors
  • Donnet, Sophie
  • Rivoirard, Vincent
  • Rousseau, Judith
Publication Date
Feb 16, 2018
Source
Kaleidoscope Open Archive
Keywords
Language
English
License
Unknown
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Abstract

This paper studies nonparametric estimation of parameters of multivariate Hawkes processes. We consider the Bayesian setting and derive posterior concentration rates. First rates are derived for L1-metrics for stochastic intensities of the Hawkes process. We then deduce rates for the L1-norm of interactions functions of the process. Our results are exemplified by using priors based on piecewise constant functions, with regular or random partitions and priors based on mixtures of Betas distributions. Numerical illustrations are then proposed with in mind applications for inferring functional connec-tivity graphs of neurons.

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