Previous research has shown the potential value of Bayesian methods in fMRI (functional magnetic resonance imaging) analysis. For instance, results from Bayes factor-applied second-level analysis showed a higher hit rate compared with frequentist analysis, suggesting greater sensitivity. Although method reported more positives as result sensitivity, it was able to maintain reasonable level sele...

A new approach to Bayesian reconstruction is introduced in which the prior probability distribution is endowed with an inherent geometrical flexibility. This flexibility is achieved through a warping of the coordinate system of the prior distribution into that of the reconstruction. This warping allows various degrees of mismatch between the assumed prior distribution and the actual distributio...

In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the ...

In this paper we consider Bayesian inference using training data combined with prior information. The prior information considered is response and causality information which gives constraints on the posterior distribution. It is shown how these constraints can be expressed in terms of the prior probability distribution, and how to perform the computations. Further, it is discussed how this pri...

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribut...

Motivation & Previous Work: A common objective in learning a model from data is to recover its network structure, while the model parameters are of minor interest. For example, we may wish to recover regulatory networks from high-throughput data sources. Regularization is essential when learning from finite data sets. It provides not only smoother estimates of the model parameters compared to m...

OBJECTIVE To evaluate the efficacy and safety of onabotulinumtoxinA in adolescents with primary axillary hyperhidrosis. METHODS This 52-week, multicenter, nonrandomized, open-label study was conducted in 141 adolescents ages 12 to 17 years with severe primary axillary hyperhidrosis. Patients could receive up to six treatments with onabotulinumtoxinA (50 U per axilla), with re-treatment occurr...