7 citations · 25 across the 7 of their papers we have counts for
7 papers
A Bounded Measure for Estimating the Benefit of Visualization: Theoretical Discourse and Conceptual Evaluation
Min Chen, Mateu Sbert
Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to es…
A Bounded Measure for Estimating the Benefit of Visualization: Case Studies and Empirical Evaluation
Min Chen, Alfie Abdul-Rahman, Deborah Silver +1
Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss. At a glance, there seem to be numerous opportunities fo…
Is the Chen-Sbert Divergence a Metric?
Min Chen, Mateu Sbert
Recently, Chen and Sbert proposed a general divergence measure. This report presents some interim findings about the question whether the divergence measure is a metric or not. It…
A Bounded Measure for Estimating the Benefit of Visualization
Min Chen, Mateu Sbert, Alfie Abdul-Rahman +1
Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to es…
On the Upper Bound of the Kullback-Leibler Divergence and Cross Entropy
Min Chen, Mateu Sbert
This archiving article consists of several short reports on the discussions between the two authors over the past two years at Oxford and Madrid, and their work carried out during…
Generalizing the Balance Heuristic Estimator in Multiple Importance Sampling
Mateu Sbert, Víctor Elvira
In this paper, we propose a novel and generic family of multiple importance sampling estimators. We first revisit the celebrated balance heuristic estimator, a widely used Monte Ca…