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SAC
2016
ACM

Computing with Fisher geodesics and extended exponential families

4 years 3 months ago
Computing with Fisher geodesics and extended exponential families
Abstract 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. Keywords Extended exponential family · Fisher geodesic · Fisher metric · Information geometry · Markov...
Frank Critchley, Paul Marriott
Added 09 Apr 2016
Updated 09 Apr 2016
Type Journal
Year 2016
Where SAC
Authors Frank Critchley, Paul Marriott
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