Nature

Bayesian Inference Methods in Nonparametric Statistics

Bayesian inference in nonparametric settings offers a coherent framework for learning complex, infinite-dimensional objects, such as probability densities, regression functions or solutions to inverse ...
JSTOR Daily

Bayesian non-parametric generation of fully synthetic multivariate categorical data in the presence of structural zeros

Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 181, No. 3 (2018), pp. 635-647 (13 pages) Statistical agencies are increasingly adopting synthetic data methods for ...
Phys.org

Advances in Bayesian methods for big data

In the Big Data era, many scientific and engineering domains are producing massive data streams, with petabyte and exabyte scales becoming increasingly common. Besides the explosive growth in volume, ...
Nature

Evaluation of alternative methods for estimating the precision of REML-based estimates of variance components and heritability

Residual Maximum Likelihood (REML) analysis is the most widely used method to estimate variance components and heritability. This method is based on large sample theory under the assumption that the ...
JSTOR Daily

BAYESIAN NONPARAMETRIC INFERENCE ON THE STIEFEL MANIFOLD

The Stiefel manifold Vp,d is the space of all d × p orthonormal matrices, with the d−1 hypersphere and the space of all orthogonal matrices constituting special cases. In modeling data lying on the ...