In the context of testing general relativity with gravitational waves, constraints obtained multiple events are typically combined either through a hierarchical formalism or though multiplicative Bayes factor. We show that well-known dependence factors on analysis priors in regions parameter space without likelihood support can lead to strong confidence favor incorrect conclusions when one empl...

This note considers estimation of the mean of a multivariate Gaussian distribution with known variance within the Minimum Message Length (MML) framework. Interestingly, the resulting MML estimator exactly coincides with the positive-part JamesStein estimator under the choice of an uninformative prior. A new approach for estimating parameters and hyperparameters in general hierarchical Bayes mod...

An efficient hierarchical approach for image multi-level thresholding is proposed based on the maximum entropy principle and Bayes’ formula, in which no assumptions of the image histogram are made. Five forms of conditional probability distributions are employed for optimal threshold determination. Our experiments demonstrate that the proposed method is effective and achieves a significant impr...

This article presents a variational Bayes inference for normalized Gaussian network, which is a kind of mixture models of local experts. In order to search for the optimal model structure, we develop a hierarchical model selection method. The performance of our method is evaluated by using function approximation and nonlinear dynamical system identification problems. Our method achieved better ...