Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

ABSTRACT A broad class of models that routinely appear in several fields can be expressed
as partially or fully discretized Gaussian linear regressions. Besides including classical …

Ever-increasing computational power, along with ever–more sophisticated statistical
computing techniques, is making it possible to fit ever–more complex statistical models …

These notes aim at presenting an overview of Bayesian statistics, the underlying concepts
and application methodology that will be useful to astronomers seeking to analyse and …

We present results obtained by applying our BAyesian HierArchical Modeling for the
Analysis of Supernova cosmology (BAHAMAS) software package to the 740 …

Multinomial probit models are routinely-implemented representations for learning how the
class probabilities of categorical response data change with p observed predictors. Although …

The partially collapsed Gibbs (PCG) sampler offers a new strategy for improving the
convergence of a Gibbs sampler. PCG achieves faster convergence by reducing the …

Bayesian Tail Risk Interdependence Using Quantile Regression Page 1 Bayesian Analysis (2015)
10, Number 3, pp. 553–603 Bayesian Tail Risk Interdependence Using Quantile Regression …

A latent Dirichlet allocation (LDA) model is a machine learning technique to identify latent
topics from text corpora within a Bayesian hierarchical framework. Current popular …

The concept of virtual power plant (VPP) has been proposed to manage distributed
renewable energy sources as packaging to engage in the energy and reserve planning on …