We provide a short overview of importance sampling—a popular sampling tool used for Monte
Carlo computing. We discuss its mathematical foundation and properties that determine …

We show that rate-adaptive multivariate density estimation can be performed using Bayesian
methods based on Dirichlet mixtures of normal kernels with a prior distribution on the …

Gaussian processes are widely used in nonparametric regression, classification and
spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties. …

We establish weak and strong posterior consistency of Gaussian process priors studied by
Lenk [1988. The logistic normal distribution for Bayesian, nonparametric, predictive densities. …

A wide variety of priors have been proposed for nonparametric Bayesian estimation of
conditional distributions, and there is a clear need for theorems providing conditions on the prior …

Minimax $L_{2}$ risks for high-dimensional nonparametric regression are derived under two
sparsity assumptions: (1) the true regression surface is a sparse function that depends only …

In the spirit of modeling inference for microarrays as multiple testing for sparse mixtures, we
present a similar approach to a simplified version of quantitative trait loci (QTL) mapping. …

We introduce a semi-parametric Bayesian framework for a simultaneous analysis of linear
quantile regression models. A simultaneous analysis is essential to attain the true potential of …

We provide sufficient conditions under which a Dirichlet location-scale mixture of normal prior
achieves weak and strong posterior consistency at a true density. Our conditions involve …

How the brain preserves information about multiple simultaneous items is poorly understood.
We report that single neurons can represent multiple stimuli by interleaving signals across …