Statistics and Computing

, Volume 26, Issue 1–2, pp 325–332 | Cite as

Computing with Fisher geodesics and extended exponential families

  • F. Critchley
  • P. MarriottEmail author


Recent progress using geometry in the design of efficient Markov chain Monte Carlo (MCMC) algorithms have shown the effectiveness of the Fisher Riemannian structure. Furthermore, the theory of the underlying geometry of spaces of statistical models has made an important breakthrough by extending the classical theory on exponential families to their closures, the so-called extended exponential families. This paper looks at the underlying geometry of the Fisher information, in particular its limiting behaviour near boundaries, which illuminates the excellent behaviour of the corresponding geometric MCMC algorithms. Further, the paper shows how Fisher geodesics in extended exponential families smoothly attach the boundaries of extended exponential families to their relative interior. We conjecture that this behaviour could be exploited for trans-dimensional MCMC algorithms.


Extended exponential family Fisher geodesic Fisher metric Information geometry Markov chain Monte Carlo 



Thanks to EPSRC for grants EP/E017878/1 and EP/L010429/1


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsThe Open UniversityMilton KeynesUK
  2. 2.Department of Statistics and Actuarial ScienceUniversity of WaterlooWaterlooCanada

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