Explanation of Bayesian Networks and Influence Diagrams in Elvira

Abstract

Abstract-Bayesian networks and influence diagrams are probabilistic graphical models widely used for building diagnosisand decision-support expert systems. Explanation of both the model and the reasoning is important for debugging these models, for alleviating users' reluctance to accept their advice, and for using them as tutoring systems. This paper describes some explanation options for Bayesian networks and influence diagrams that have been implemented in Elvira and how they have been used for building medical models and for teaching probabilistic reasoning to pre-and post-graduate students.

A visual recommender tool in a collaborative learning experience

“…Therefore, the open learner model strategy requires that the information shown is self-explanatory. So, the development of explanation facilities is crucial for the acceptance of expert systems (Lacave et al, 2007). Humans do not usually accept the advice provided by a computer if they cannot understand how the system reasoned to reach the conclusions.…”

Building a Bayesian Network Model Based on the Combination of Structure Learning Algorithms and Weighting Expert Opinions Scheme

“…Elvira is a tool for building and evaluating graphical probabilistic models (Lacave et al, 2007). It is a non web-based application.…”

A multivariate probabilistic method for comparing two clinical datasets

“…Influence-tracing methods aim to describe the influence that evidence has on unobserved variables in terms of the relationships between the variables. The literature contains methods for describing such influences both qualitatively [3] and quantitatively using differences [7], log-ratios [8], and other functions such as cross-entropy [11,12] for comparing conditional probabilities. We follow a similar approach to quantitatively compare probabilities that are obtained by conditioning on Z.…”