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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Matsuda, T., Watanabe, S. (2006). Analytic Equivalence of Bayes a Posteriori Distributions. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_12
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DOI: https://doi.org/10.1007/11840817_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38625-4
Online ISBN: 978-3-540-38627-8
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