Riquelme Flores, Sofía Isidora2025-08-012025-08-012025-07https://repositorio.usm.cl/handle/123456789/75846Bayesian Networks are widely used to model probabilistic relationships between variables through DAGs. However, multiple Bayesian Networks can represent the same set of conditional independencies, forming what is known as a MEC. This presents a challenge in model evaluation, as networks within the same MEC are statistically indistinguishable based on observational data alone. This work investigates whether the log-likelihood function, a standard metric for assessing model fit, remains invariant across all Bayesian Networks in a given MEC. After learning the CPDAG of a Bayesian Network from data, a method is implemented to enumerate DAGs in the corresponding MEC, converting each DAG to a Bayesian Network and compute the log-likelihood for each structure using Bayesian parameter estimation. Structural modifications are also introduced to produce networks outside the original MEC, enabling comparison of log-likelihood values across different equivalence classes. Experiments conducted on benchmark networks (Asia, ALARM, Hepar2) confirm that log-likelihood values remain constant within each MEC and vary significantly when the structure is modified to break v-structures. These findings suggest that log-likelihood invariance can serve as a reliable indicator of MEC membership for Bayesian Networks, allowing for more efficient model evaluation and structure learning without full enumeration.39 páginasenAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Bayesian networksMarkov equivalence classLog-likelihood35609002881214 Educación de calidad9 Industria, innovación e infraestructura17 Alianzas para lograr los objetivos