Dimensionality reduction is important for the high-dimensional nature of data in the process
industry, which has made latent variable modeling methods popular in recent years. By …

Accurate wind power forecasting has great practical significance for the safe and
economical operation of power systems. In reality, wind power data are recorded at high …

Accurate wind speed forecasting is helpful for reducing the instantaneous fluctuation of
voltage, and also has great practical significance on power dispatching and plan. There are …

Virtual sensors are mathematical methods that describe the dependence of primary
variables on secondary variables. For the majority of industrial processes with particularly …

We present a probabilistic model for robust factor analysis and principal component analysis
in which the observation noise is modeled by Student-t distributions in order to reduce the …

Factors models are commonly used to analyze high-dimensional data in both single-study
and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte …

The Bayesian information criterion (BIC) is one of the most popular criteria for model
selection in finite mixture models. However, it implausibly penalizes the complexity of each …

We propose VarFA, a variational inference factor analysis framework that extends existing
factor analysis models for educational data mining to efficiently output uncertainty estimation …

Structural equation models are commonly used to capture the relationship between sets of
observed and unobservable variables. Traditionally these models are fitted using frequentist …

In this study, a novel Bayesian robust mixture factor analyzer (BRMFA) is proposed to deal
with the robust multimode process modeling problem. Traditional factor analyzers with …