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Bayesian Inference for Non-Markovian Point Processes

  • Peter Guttorp
  • Thordis L. ThorarinsdottirEmail author
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 207)

Abstract

The Bayesian approach to statistical inference has in recent years become very popular, especially in the analysis of complex data sets. This is largely due to the development of Markov chain Monte Carlo methods, which expand the scope of application of Bayesian methods considerably. In this paper, we review the Bayesian contributions to inference for point processes. We focus on non-Markovian processes, specifically Poisson and related models, doubly stochastic models, and cluster models. We also discuss Bayesian model selection for these models and give examples in which Bayes factors are applied both directly and indirectly through a reversible jump algorithm.

Keywords

Poisson Process Point Process Gaussian Process Point Pattern Minke Whale 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Applied MathematicsHeidelberg UniversityHeidelbergGermany
  2. 2.University of WashingtonSeattleUSA
  3. 3.Norwegian Computing CentreOsloNorway

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