Learning Bayesian networks in complex survey sampling
Daniela Marella, Flaminia Musella, Paola Vicard · Iris (Roma Tre University) · 2014
Bayesian networks are multivariate statistical models satisfying sets of conditional independence statements. The association structure can be learnt from data by a sequence of independence and conditional independence tests using the PC algorithm. The learning process is based on assumption of independent and identically distributed observations. This assumption is almost never valid for sample surveys data since most of the commonly used survey designs employ stratification and/or cluster sampling and/or unequal selection probabilities. Here, a PC algorithm correction is proposed for taking into account the sampling design complexity.