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

Abstract

Submitted by
SALINI SILVIA
Authors
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)
Abstract
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.

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