Samenvatting
Gaussian graphical models (GGMs) are probabilistic graphical models
based on partial correlation. A GGM consists of a network of nodes (representing
the random variables) connected by edges (their partial correlation). To infer a
GGM, the inverse of the covariance matrix (the precision matrix) is required. The
main challenge is that when the number of variables is larger than the sample size,
the (sample) covariance is ill conditioned (or not invertible). Shrinkage methods
consist in regularizing the estimator of the covariance matrix to make it invertible
(and well conditioned); however, the effect of the shrinkage on the final network
topology has not been studied so far.
based on partial correlation. A GGM consists of a network of nodes (representing
the random variables) connected by edges (their partial correlation). To infer a
GGM, the inverse of the covariance matrix (the precision matrix) is required. The
main challenge is that when the number of variables is larger than the sample size,
the (sample) covariance is ill conditioned (or not invertible). Shrinkage methods
consist in regularizing the estimator of the covariance matrix to make it invertible
(and well conditioned); however, the effect of the shrinkage on the final network
topology has not been studied so far.
| Originele taal-2 | English |
|---|---|
| Pagina's | 281-284 |
| Aantal pagina's | 4 |
| Status | Published - 24-jul.-2020 |
| Evenement | 35th International Workshop on Statistical Modelling - Bilbao, Spain Duur: 20-jul.-2020 → 24-jul.-2020 Congresnummer: 35 https://wp.bcamath.org/iwsm2020/ |
Conference
| Conference | 35th International Workshop on Statistical Modelling |
|---|---|
| Verkorte titel | IWSM 2020 |
| Land/Regio | Spain |
| Stad | Bilbao |
| Periode | 20/07/2020 → 24/07/2020 |
| Internet adres |