Creative Commons Attribution-ShareAlike 4.0 International
This œuvre, Laplace's rule of succession in information geometry, by Yann Ollivier is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license.

Laplace's rule of succession in information geometry


Laplace's rule of succession in information geometry
Publication details: 
When observing data x1, . . . , x t modelled by a probabilistic distribution pθ(x), the maximum likelihood (ML) estimator θML = arg max θ Σti=1 ln pθ(x i ) cannot, in general, safely be used to predict xt + 1. For instance, for a Bernoulli process, if only “tails” have been observed so far, the probability of “heads” is estimated to 0. (Thus for the standard log-loss scoring rule, this results in infinite loss the first time “heads” appears.)
Source et DOI
Vidéo
Voir la vidéo
Laplace's rule of succession in information geometry
Groupes / audience: