The classical modeling of spatial extremes relies on asymptotic models (ie, max‐stable or r‐
Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at …

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 …

The unknown parameters (variance, smoothness, and covariance length) of a spatial
covariance function can be estimated by maximizing the joint Gaussian log-likelihood …

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

We propose a hierarchical tensor-network approach for approximating high-dimensional
probability density via empirical distribution. This leverages randomized singular value …

Modern methods for Bayesian regression beyond the Gaussian response setting are often
computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent …

With the rapid development of the Internet of Things and cloud computing, HBase has
become a good choice for massive data storage, and is efficient in reading and writing data …

Age of information (AoI) is an effective performance metric for time-critical industrial Internet
of things (IIoT) applications. We investigate status prediction and data aggregation with …

Predictive models for binary data are fundamental in various fields, and the growing
complexity of modern applications has motivated several flexible specifications for modeling …

Physics-based covariance models provide a systematic way to construct covariance models
that are consistent with the underlying physical laws in Gaussian process analysis. The …