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.

Learning Bayesian networks in complex survey sampling / Marella, D.; Musella, F.; Vicard, P.. - (2014). (Intervento presentato al convegno 47th SIS Scientific Meeting of the Italian Statistica Society tenutosi a Cagliari).

Learning Bayesian networks in complex survey sampling.

Marella D.;Vicard P.
2014

Abstract

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.
2014
47th SIS Scientific Meeting of the Italian Statistica Society
complex sample survey, decomposability, PC algorithm
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Learning Bayesian networks in complex survey sampling / Marella, D.; Musella, F.; Vicard, P.. - (2014). (Intervento presentato al convegno 47th SIS Scientific Meeting of the Italian Statistica Society tenutosi a Cagliari).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1617541
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