This paper is an attempt to bridge the conceptual gaps between researchers working on the
two widely used approaches based on positive definite kernels: Bayesian learning or …

This paper is to provide practical options for prognostics so that beginners can select
appropriate methods for their fields of application. To achieve this goal, several popular …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers'
knowledge of and confidence in statistical modeling. Reflecting the need for even minor …

This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that
aims to learn an interpretable representation of images, disentangled with respect to three …

Hamiltonian dynamics can be used to produce distant proposals for the Metropolis
algorithm, thereby avoiding the slow exploration of the state space that results from the …

These lecture notes highlight the mathematical and computational structure relating to the
formulation of, and development of algorithms for, the Bayesian approach to inverse …

The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

Many problems arising in applications result in the need to probe a probability distribution
for functions. Examples include Bayesian nonparametric statistics and conditioned diffusion …

Cross-national analyses test hypotheses about the drivers of variation in national outcomes.
However, since nations are connected in various ways, such as via spatial proximity and …