In this paper, we obtain the main term of the average stochastic complexity for certain complete bipartite graph-type spin models in Bayesian estimation. We study the Kullback function of the spin model by using a new method of eigenvalue analysis first and use a recursive blowing up process for obtaining the maximum pole of the zeta function which is defined by using the Kullback function. The papers [1,2] showed that the maximum pole of the zeta function gives the main term of the average stochastic complexity of the hierarchical learning model. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Aoyagi, M., & Watanabe, S. (2007). Resolution of singularities and stochastic complexity of complete bipartite graph-type spin model in Bayesian estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4617 LNAI, pp. 443–454). Springer Verlag. https://doi.org/10.1007/978-3-540-73729-2_42
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