Abstract: Two-sample hypothesis testing is a common practice in many fields of science, where the goal is to identify whether a set of observations and a set of training data are drawn from the same ...

A nonparametric Bayesian approach is developed to determine quantum potentials from empirical data for quantum systems at finite temperature. The approach combines the likelihood model of quantum ...

Sample size determination is among the most commonly encountered tasks in statistical practice. A broad range of frequentist and Bayesian methods for sample size determination can be described as ...

Nonparametric approaches to density estimation are discussed from a Bayesian perspective. Being in general non-Gaussian the resulting models have to be solved by discretization. A numerical example ...

Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps ...

We introduce an approach for efficient Markov chain Monte Carlo (MCMC) sampling for challenging high-dimensional distributions in sparse Bayesian learning (SBL). The core innovation involves using ...

Parameter estimation in differential equation models is a critical endeavour in the mathematical modelling of dynamic systems. Such models, represented by ordinary differential equations (ODEs), ...