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Estimating Real Log Canonical Thresholds
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Evaluation of the marginal likelihood plays an important role in model selection problems. The widely applicable Bayesian information criterion (WBIC) and singular Bayesian information criterion (sBIC) give approximations to the log marginal likelihood, which can be applied to both regular and singular models. When the real log canonical thresholds are known, the performance of sBIC is considered to be better than that of WBIC, but only few real log canonical thresholds are known. In this paper, we propose a new estimator of the real log canonical thresholds based on the variance of thermodynamic integration with an inverse temperature. In addition, we propose an application to make sBIC widely applicable. Finally, we investigate the performance of the estimator and model selection by simulation studies and application to real data.
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Cited by 1 Pith paper
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Upper Bounds for Local Learning Coefficients of Three-Layer Neural Networks
An upper-bound formula for local learning coefficients at singular points of three-layer networks is derived via blow-ups and matches known exact coefficients when the input dimension is one.
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