This article reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three di...

Changepoint models enjoy a wide appeal in variety of disciplines to model the heterogeneity ordered data. Graphical influence diagnostics characterize single observations on changepoint are, however, lacking. We address this gap by developing framework for investigating instabilities segmentations and assessing various outputs analysis. construct graphical diagnostic plots that allow practition...

This paper deals with the issue of concept-drift in machine learning context high dimensional problems. In contrast to previous concept drift detection methods, this application does not depend on model use for a specific target variable, but rather, it attempts assess as an independent characteristic evolution dataset. major achievement enables data be tested presence drift, independently prob...

In recent years, machine learning (ML) algorithms have been deployed in safety-critical and high-stake decision-making, where the fairness of is paramount importance. Fairness ML centers on detecting bias towards certain demographic populations induced by an classifier proposes algorithmic solutions to mitigate with respect different definitions. To this end, several verifiers proposed that com...

Complex probabilistic models are difficult to evaluate not only for consistency with the domain theory but also for novelty and significance. In this paper we describe the current visual representations of Bayesian networks and then present an implementation containing enhancements to these visualizations by integrating several well known techniques. We then apply our approach in an exploration...