The association structure of a Bayesian network can be known in advance by subject matter knowledge or have to be learned from a database. In case of data driven learning, one of the most known procedures is the PC algorithm where the structure is inferred carrying out several independence tests under the assumption of independent and identically distributed observations. In practice, sample selection in surveys involves more complex sampling designs. In this paper, a modified version of the PC algorithm is proposed for inferring casual structure from complex survey data.

Marella, D., Vicard, P. (2016). PC algorithm from complex sample data. In Proceedings of the 48th SIS Scientific Meeting of the Italian Statistical Society.

PC algorithm from complex sample data

MARELLA, Daniela;VICARD, Paola
2016-01-01

Abstract

The association structure of a Bayesian network can be known in advance by subject matter knowledge or have to be learned from a database. In case of data driven learning, one of the most known procedures is the PC algorithm where the structure is inferred carrying out several independence tests under the assumption of independent and identically distributed observations. In practice, sample selection in surveys involves more complex sampling designs. In this paper, a modified version of the PC algorithm is proposed for inferring casual structure from complex survey data.
9788861970618
Marella, D., Vicard, P. (2016). PC algorithm from complex sample data. In Proceedings of the 48th SIS Scientific Meeting of the Italian Statistical Society.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/307522
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