64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

A Meta-Model for Predicting the Quality of Knowledge Elicitation Sessions


Hussein Jouni


  • L
    Lionel Jouffe
  • P
    Philippe Bastien
  • A
    Alban Ott
  • L
    Léopold Carron
  • M
    Mathieu Le Tertre
  • T
    Telma Da Silva


64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Abstract

Keywords: bayesian, elicitation, modeling

Session: CPS 18 - Statistical modelling I

Monday 17 July 4 p.m. - 5:25 p.m. (Canada/Eastern)


Capitalizing on expert knowledge can be useful for a company. It can be for transmitting all the know-how on a given field, incorporating technical aspects for decision making, or building causal models for doing predictions. This knowledge can be represented through a Bayesian Network [1] to introduce uncertainty on the phenomenon, and, combined with Data, its performance can be improved. Elicitation is done thanks to sessions where experts works together to build models with a facilitator and a modeler. It is asked the experts to be available for a given amount of time, which can be large (several days) and with a risk that at the end of the sessions, they will not be able to have a satisfying tool. In the context of multi-project management, we propose a tool to assess the probability of success of Elicitation sessions on a given problem. This tool is obtained thanks to the Elicitation of a Bayesian Network [1] (meta-model), quantified with prior distributions.

After a brief description of the context (elicitation modeling prioritization), we explain how we were been able to elicit the meta-model.
First, a theoretical justification of the Bayesian network structure & nodes combination functions is done. Then, we detail the different dimensions and the output, followed by a demo of the app that has been generated with the model.

[1] Probabilistic Graphical Models: Principles and Techniques. D. Koller, and N. Friedman. Adaptive computation and machine learning MIT Press, (2009 )