Webinar: What is Importance?

Recorded on August 28, 2019.

Context & Motivation

Everyone knows the meaning of “importance,” right? “What is important?” is a common question in daily life, and it is presumably the most common question in research. It’s about understanding what matters within the context of a given domain.

Upon entering the world of statistics and analytics, we encounter a myriad of measures all related to importance, e.g., correlation, weight, significance, indirect/direct effect size, temporal/contemporaneous effects, unit effect, standardized effect, Bayes factor, Mutual Information, KL-Divergence, contribution, elasticity, etc. Additionally, some of these measures should not be used in isolation but instead need to be seen in conjunction with other quantities, such as joint probability, for decision-making purposes. This highlights that “importance” is not a narrowly defined concept; instead, it covers a broad and diverse spectrum of notions.

While none of these measures are tied to Bayesian networks, we employ this framework to explain major and minor differences between these concepts. More specifically, we aim to develop intuition for all of the above concepts using Bayesian network models learned from data. Our objective is to understand in which contexts each measure of importance is most appropriate.

Presentation Video

Presentation Materials

About the Presenter

Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan, which included assignments in North America, Europe, and Asia.

Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning.

Recently, Stefan and his colleague Dr. Lionel Jouffe co-authored Bayesian Networks & BayesiaLab: A Practical Introduction for Researchers, which is now available as an e-book.