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.
Vitale, V., Vicard, P. (2018). PC Algorithm for Gaussian Copula Data. In Book of short Papers SIS 2018 (pp.789-794). Torino : Pearson.
PC Algorithm for Gaussian Copula Data
Vincenzina Vitale;Paola Vicard
2018-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
