Analytic equivalence of bayes a posteriori distributions

Authors:
Takeshi Matsuda;Sumio Watanabe
Affiliations:
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 2
Cited 1

Algebraic Analysis for Nonidentifiable Learning Machines

Neural Computation
Stochastic complexities of reduced rank regression in Bayesian estimation

Neural Networks

On a singular point to contribute to a learning coefficient and weighted resolution of singularities

ICANN'07 Proceedings of the 17th international conference on Artificial neural networks

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Abstract

A lot of learning machines which have hidden variables or hierarchical structures are singular statistical models. They have singular Fisher information matrices and different learning performance from regular statistical models. In this paper, we prove mathematically that the learning coefficient is determined by the analytic equivalence class of Kullback information, and show experimentally that the stochastic complexity by the MCMC method is also given by the equivalence class.