Resumen: The present paper focuses on the study of the conditions under which the covariance matrix of a multivariate Gaussian distribution is totally positive, paying particular attention to multivariate Gaussian distributions that are Gaussian Markov Random Fields. More specifically, it is proven that, if the graph over which the Gaussian Markov Random Field is defined consists of path graphs and the covariances between adjacent variables on the graph are non-negative, then there always exists a reordering of the variables that renders the resulting covariance matrix totally positive. Moreover, this reordering is identified and some cases for which the conditions for the covariance matrix of a multivariate Gaussian distribution to be totally positive are necessary and sufficient are provided. Idioma: Inglés DOI: 10.1016/j.cam.2023.115098 Año: 2023 Publicado en: Journal of Computational and Applied Mathematics 430 (2023), 115098[10 pp.] ISSN: 0377-0427 Factor impacto JCR: 2.1 (2023) Categ. JCR: MATHEMATICS, APPLIED rank: 53 / 332 = 0.16 (2023) - Q1 - T1 Factor impacto CITESCORE: 5.4 - Computational Mathematics (Q1) - Applied Mathematics (Q1)