We introduce "NPB-REC", a non-parametric fully Bayesian framework, for MRI reconstruction from undersampled data with uncertainty estimation. We use Stochastic Gradient Langevin Dynamics during training to characterize the posterior distribution of the network parameters. This enables us to both improve...
Multi-modal distance metric learning: A bayesian non-parametric approach. In European Conference on Computer Vision, pages 63-77. Springer, 2014.Behnam Babagholami-Mohamadabadi, Seyed Mahdi Roostaiyan, Ali Zarghami, and Mahdieh Soleymani Baghshah, "Multi-modal distance metric learning: Abayesian ...
This paper proposes a non-parametric Bayesian approach to detect the change-points of intensity rates in the recurrent-event context and cluster subjects by the change-points. Recurrent events are commonly observed in medical and engineering research. The event counts are assumed to follow a non-...
Although frequency estimation is a nonlinear parametric problem, it can be cast in a non-parametric framework. By assigning a natural a priori probability to the unknown frequency, the covariance of the prior signal model is found to admit an eigenfunction expansion alike the famous prolate spheroid...
(2002) proposed a non-parametric approach to a buying–selling problem with the finite time horizon. In addition to selling or buying models, further applications can be found in the literature concerning job search (Lippman and McCall, 1976; Stein et al., 2003), engineering problems (David,...
Multi-Modal Distance Metric Learning: A Bayesian Non-parametric Approach In many real-world applications (e.g. social media application), data usually consists of diverse input modalities that originates from various heterogeneo... B Babagholami-Mohamadabadi,SM Roostaiyan,A Zarghami,... - ...
Basically, this article introduces non-parametric Bayesian regression to get the conditional posterior distribution of the CVD risk probability. And it also emphasizes a new sampling approach, which improves efficiency of traditional case-base sampling. Based on traditional case-base sampling, this articl...
It was however shown in previous studies, that a more advanced non-parametric approach which com- bines Bayesian linear regression and GPs is able to achieve higher performance [16] assuming that some of the parameters are known. In our work, we show that knowledge of such parameters prior ...
[23,24], GPs are particularly adept at modeling and analyzing complex, non-linear, and noisy data. Being a form of non-parametric Bayesian modeling, GPs find applications in regression, classification, optimization, and uncertainty quantification. In the field of cosmology, GPs have been ...
In this paper, we introduce a frequentist, non-Bayesian parametric model of the problem of missing-mass estimation. We introduce the concept of missing-mass unbiasedness by using the Lehmann unbiasedness definition. We derive a non-Bayesian CCRB-type lower bound on the missing-mass MSE (mmMSE)...