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The Variational Bayesian Inference for Network Autoregression Models
We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the network autoregression framework.
Finding Optimal Points for Expensive Functions Using Adaptive RBF-Based Surrogate Model Via Uncertainty Quantification
Then it utilizes a model-guided selection criterion to identify a new point from a candidate set for function evaluation.
Greedy Active Learning Algorithm for Logistic Regression Models
Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model, comparing with that of the classifier trained with all variables and a full data set.
Multiple-Instance Logistic Regression with LASSO Penalty
In this work, we consider a manufactory process which can be described by a multiple-instance logistic regression model.
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