This paper reviews predictive inference and feature selection for generalized linear models
with scarce but high-dimensional data. We demonstrate that in many cases one can benefit …

Bayesian inference is a powerful tool for combining information in complex settings, a task of
increasing importance in modern applications. However, Bayesian inference with a flawed …

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all
observations, model parameters, and model structure using probability theory. Probabilistic …

Counterfactual explanations (CEs) are a practical tool for demonstrating why machine
learning classifiers make particular decisions. For CEs to be useful, it is important that they …

In this research, solar cell capacitance simulator-one-dimensional (SCAPS-1D) software
was used to build and probe nontoxic Cs-based perovskite solar devices and investigate …

The concepts of Bayesian prediction, model comparison, and model selection have
developed significantly over the last decade. As a result, the Bayesian community has …

Projection predictive inference is a decision theoretic Bayesian approach that decouples
model estimation from decision making. Given a reference model previously built including …

Background The intersection of nutrition and technology gave birth to the research of food
recommendation system (FRS), which marked the transformation of traditional diet to a more …

Subset selection is a valuable tool for interpretable learning, scientific discovery, and data
compression. However, classical subset selection is often avoided due to selection …

Existing explanation algorithms have found that, even if deep models make the same correct
predictions on the same image, they might rely on different sets of input features for …