Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Regression analysis constitutes an important tool for investigating the effect of explanatory
variables on response variables. When outliers and bias errors are present, the weighted …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

This article is concerned with data-driven realization of fault detection (FD) for nonlinear
dynamic systems. In order to identify and parameterize nonlinear Hammerstein models …

The analysis of power systems with a significant share of renewable generation using
probabilistic tools is essential to appropriately consider the impact that the variability and …

Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …

In this paper, we consider the identification problem for nonlinear state-space models with
skewed measurement noises. The generalized hyperbolic skew Student'st (GHSkewt) …

Probabilistic latent variable regression models have recently caught much attention in the
process industry, particularly for soft sensor applications. One of the main challenges for …

Abstract Machine learning (ML) methods are becoming popular tools for predicting and
designing novel materials. In particular, neural network (NN) is a promising ML method …

A variational Bayesian (VB) approach to the identification of linearly parameterized
nonlinear state-space models (LP-NSSMs) is developed in this article. Conjugate priors over …