Publications (2020-2021)

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Browsing Publications (2020-2021) by Subject "Bayesian Networks"

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    CBTS-SGL Webinar - Cool Things That One Can Do With Graphical Probabilistic Models - Dr. Marek Drudzel
    (Stochastic Geomechanics Laboratory, 2021-01-12) Drudzel, Marek; Medina-Cetina, Zenon; Pompelli, Gregory; Cochran, Matt; Olivares, Miriam; Perez-Patron, Maria Jose; United States Department of Homeland Security (DHS)
    On January, 2021 the CBTS COE and the Stochastic Geomechanics Laboratory (SGL) organized a webinar with Dr. Marek Drudzel as a guest speaker to give a presentation on graphical probabilistic models, and their potential applications to real-world problems. The talk reviewed briefly the theoretical foundations of Bayesian networks and their applications to practical problems. Dr. Drudzel showed several flavors of Bayesian networks, such as discrete, continuous, and hybrid networks, as well as qualitative, and dynamic networks. He also reviewed some applications of Bayesian network models in diagnosis, prognosis, data analysis, and strategic planning. Part of the talk was based on live demonstration of the concepts using GeNIe, a software developed originally in Dr. Druzdzel's lab at the University of Pittsburgh.
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    R7 - Internal Report on Bayesian Risk Assessment & Management Model Development V0.0
    (Stochastic Geomechanics Laboratory, 21-Feb) Medina-Cetina, Zenon; Pompelli, Gregory; Cochran, Matt; Alvarado, Juan Pablo; Duran Sierra, Guillermo; Zarate-Losoya, Enrique; Allen, Alexi; United States Department of Homeland Security (DHS)
    Internal report on the development steps for a Risk Assessment and Management model using Bayesian Networks. The objectives of the model include: mapping qualitatively participating processes needed to simulate prognosis and diagnosis scenarios of social, economic and environmental impacts posed by COVID19 on the U.S. trade supply chain infrastructure. To address the public health impacts of the COVID-19 pandemic on the U.S.- Mexico health supply chain systems for health infrastructure and for the health of the workforce, considering current and emerging regional social, economic and environmental Risks. To generate risk-mitigating strategies based on resiliency and sustainability supported by evidence collection and the associate risk assessment model, to address causes and effects posed by COVID19 on the U.S. trade supply chain infrastructure, U.S.- Mexico health supply chain systems for health infrastructure, and for health of the workforce between U.S. - Mexico.
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    R7 - Internal Report on Bayesian Risk Assessment & Management Model Development V1.0
    (Stochastic Geomechanics Laboratory, 2021-05-24) Medina-Cetina, Zenon; Pompelli, Gregory; Cochran, Matt; Alvarado, Juan Pablo; Duran Sierra, Guillermo; Zarate-Losoya, Enrique; United States Department of Homeland Security (DHS)
    The version 0.0 of the Bayesian Networks (BN) model was updated to include variables and processes identified during the development of R7 project, guided by the variables present in Social Vulnerability Indexes from both Mexico and The U.S. This internal report lists the indexes that complement the BN model, as well as the updated structure of the components, groups, and subgroups of the model.