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Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities
 

Summary: Tractable Nonparametric Bayesian Inference in Poisson Processes
with Gaussian Process Intensities
Ryan Prescott Adams rpa23@cam.ac.uk
Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK
Iain Murray murray@cs.toronto.edu
Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4
David J.C. MacKay mackay@mrao.cam.ac.uk
Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK
Abstract
The inhomogeneous Poisson process is a
point process that has varying intensity
across its domain (usually time or space). For
nonparametric Bayesian modeling, the Gaus-
sian process is a useful way to place a prior
distribution on this intensity. The combina-
tion of a Poisson process and GP is known as
a Gaussian Cox process, or doubly-stochastic
Poisson process. Likelihood-based inference
in these models requires an intractable in-
tegral over an infinite-dimensional random

  

Source: Adams, Ryan Prescott - Department of Electrical and Computer Engineering, University of Toronto

 

Collections: Computer Technologies and Information Sciences