Nature

Bayesian Nonparametric Mixture Models

Bayesian nonparametric mixture models represent a powerful statistical framework that extends traditional mixture modelling by allowing the number of mixture components to be inferred from the data ...
JSTOR Daily

Risk Assessment for Toxicity Experiments with Discrete and Continuous Outcomes: A Bayesian Nonparametric Approach

Journal of Agricultural, Biological, and Environmental Statistics, Vol. 22, No. 4 (December 2017), pp. 585-601 (17 pages) We present a Bayesian nonparametric modeling approach to inference and risk ...
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 ...
Nature

Kernel Density Estimation and Nonparametric Methods

Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...