The PC algorithm is the most popular algorithm used to infer the structure of a Bayesian network directly from data. For Gaussian distributions, it infers the network structure using conditional independence tests based on Pearson correlation coefficients. Here, we propose two modified versions of PC, the R-vine PC and D-vine PC algorithms, suitable for elliptical copula data. The correlation matrix is inferred by means of the estimated structure and parameters of a regular vine. Simulation results are provided, showing the very good performance of the proposed algorithms with respect to their main competitors.

PC Algorithm for Gaussian Copula Data / Vitale, Vincenzina; Vicard, Paola. - (2018), pp. 789-794. (Intervento presentato al convegno 49th SIS Scientific Meeting of the Italian Statistical Society tenutosi a Palermo).

PC Algorithm for Gaussian Copula Data

Vincenzina Vitale;
2018

Abstract

The PC algorithm is the most popular algorithm used to infer the structure of a Bayesian network directly from data. For Gaussian distributions, it infers the network structure using conditional independence tests based on Pearson correlation coefficients. Here, we propose two modified versions of PC, the R-vine PC and D-vine PC algorithms, suitable for elliptical copula data. The correlation matrix is inferred by means of the estimated structure and parameters of a regular vine. Simulation results are provided, showing the very good performance of the proposed algorithms with respect to their main competitors.
2018
49th SIS Scientific Meeting of the Italian Statistical Society
Structural learning; Bayesian networks; R-vines; Gaussian copulae; PC algorithm
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
PC Algorithm for Gaussian Copula Data / Vitale, Vincenzina; Vicard, Paola. - (2018), pp. 789-794. (Intervento presentato al convegno 49th SIS Scientific Meeting of the Italian Statistical Society tenutosi a Palermo).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1411410
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