Vitenskapelig Kapittel/Artikkel/Konferanseartikkel   2012

Guttorp, Peter; Thorarinsdottir, Thordis L.

Publikasjonsdetaljer

Sider:

79–102

År:

2012

Lenker:

FULLTEKST: http://dx.doi.org/10.1007/978-3-642-17086-7_4
DOI: doi.org/10.1007/978-3-642-17086-7_4

Del av: Advances and Challenges in Space-time Modelling of Natural Events (Springer, 2012)

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.