## 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 |
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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 |