Bayesian information criteria and smoothing parameter selection in radial basis function networks

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Estimation of oblique structure via penalized likelihood factor analysis (2013, methodology): “Hirose et al. (2011) derived a generalized Bayesian information criterion (Konishi et al. 2004) for selecting the appropriate value of η , whereas it is diﬃcult to derive generalized Bayesian model criterion in lasso-type penalization procedure.”
A comparative review of dimension reduction methods in approximate Bayesian computation (2012, methodology): “resistant tuberculosis (Luciani et al. (2009)) and an assessment of the number and size-distribution of particle inclusions in the production of clean steels (Bortot, Coles and Sisson (2007)).”
Tuning Parameter Selection for the Adaptive Lasso Using ERIC (2015, methodology): “To this end, Konishi, Ando, and Imoto (2004) formulated a BIC-type criterion for choosing smoothing parameters in radial basis function network models, by directly applying Laplace’s approximation on the joint distribution of the likelihood and the basis functions which assumed Gaussian priors…”