The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Gaussian process regression is a popular Bayesian framework for surrogate modeling of
expensive data sources. As part of a broader effort in scientific machine learning, many …

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 …

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law
Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning …

We propose a framework to identify a rich set of structural drivers of inflation in order to
understand the role of the multiple and concomitant sources of the post-pandemic inflation …

Reliability analysis of deteriorating structural systems requires the solution of time-variant
reliability problems. In the general case, both the capacity of and the loads on the structure …

Introducing inequality constraints in Gaussian processes can lead to more realistic
uncertainties in learning a great variety of real-world problems. We consider the finite …

Many industrial and scientific applications require optimization of one or more objectives by
tuning dozens or hundreds of input parameters. While Bayesian optimization has been a …

A comprehensive methodology for inference in vector autoregressions (VARs) using sign
and other structural restrictions is developed. The reduced-form VAR disturbances are …

Regression models for dichotomous data are ubiquitous in statistics. Besides being useful
for inference on binary responses, these methods serve as building blocks in more complex …