ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Bayesian Networks in Survey Data: Robustness and Sensitivity Issues

11 September 2017, 13:00 – 13:30


Submitted by
Silvia Salini (University of Milan), Ron Kenett (KPA Ltd., Raanana, Israel and University of Turin, Italy), Federica Cugnata (University Centre for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University)
Bayesian networks (BN) implement a graphical model structure known as a directed acyclic graph (DAG) that is popular in statistics, machine learning, and artificial intelligence. They enable an effective representation and computation of a joint probability distribution (JPD) over a set of random variables.
The paper focuses on the selection of a robust network structure according to different learning algorithms and the measure of arc strength using resampling techniques. Moreover, it shows how ‘what-if’ sensitivity scenarios are generated with BN using hard and soft evidence in the framework of predictive inference. Establishing a robust network structure and using it for decision support are two essential enablers for efficient and effective applications of BN to improvements of products and processes. A customer-satisfaction survey example is presented and R scripts are provided.

Return to programme