Summary Gaussian processes (GPs, Rasmussen & Williams, 2006) are Bayesian
nonparametric models that have been successfully used in applications such as …

BlackJAX is a library implementing sampling and variational inference algorithms commonly
used in Bayesian computation. It is designed for ease of use, speed, and modularity by …

This research combines deep learning with sophisticated likelihood analysis to achieve
precise detection of clean coal ash content in the domain of froth flotation through image …

Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods
gets entangled with the learning of the model hyperparameters. We improve …

Differential equations are important mechanistic models that are integral to many scientific
and engineering applications. With the abundance of available data there has been a …

This paper presents a new approach to predict the occupancy for building energy systems
(BES). A Gaussian Process (GP) is used to model the occupancy and is represented as a …

Expectation propagation (EP) is a family of algorithms for performing approximate inference
in probabilistic models. The updates of EP involve the evaluation of moments--expectations …

State-space formulations allow for Gaussian process (GP) regression with linear-in-time
computational cost in spatio-temporal settings, but performance typically suffers in the …

This paper presents an approach for efficiently approximating the inverse of Fisher
information, a key component in variational Bayes inference. A notable aspect of our …

This article introduces a method for efficiently approximating the inverse of the Fisher
information matrix, a crucial step in achieving effective variational Bayes inference. A …