Optimal control of diffusion processes is intimately connected to the problem of solving
certain Hamilton–Jacobi–Bellman equations. Building on recent machine learning inspired …

An effective approach for sampling from unnormalized densities is based on the idea of
gradually transporting samples from an easy prior to the complicated target distribution. Two …

Validation of prediction uncertainty (PU) is becoming an essential task for modern
computational chemistry. Designed to quantify the reliability of predictions in meteorology …

This paper proposes the Spatio-Temporal Crowdedness Inference Model (STCIM), a
framework to infer the passenger distribution inside the whole urban rail transit (URT) …

With the increasing use of global navigation satellite systems (GNSS) in myriad applications,
ensuring the integrity of the GNSS signals has become of paramount importance. Radio …

Previously derived Faraday rotation constraints on the volume-filling intergalactic magnetic
field (IGMF) have used analytical models that made a range of simplifying assumptions …

This paper aims to investigate critical slowing down indicators in different situations where
the system's parameters change. Variation of the bifurcation parameter is important since it …

We resurrect the infamous harmonic mean estimator for computing the marginal likelihood
(Bayesian evidence) and solve its problematic large variance. The marginal likelihood is a …

The exponentially weighted moving average (EWMA) chart for dispersion is designed to
detect structural changes in the process dispersion quickly. The various existing designs of …

Abundance indices (AIs) provide information on population abundance and trends over
time, while AI variance (AIV) provides information on reliability or quality of the AI. AIV is an …