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Mahlet G. Tadesse – författare

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4 produkter

  1. Mahlet G. Tadesse, Marina Vannucci - Handbook of Bayesian Variable Selection, Inbunden. Tillgänglighet: Lägg i varukorg

    Handbook of Bayesian Variable Selection

    Av Mahlet G. Tadesse, Marina Vannucci

    Inbunden, 2021

    2744 kr

    Lägg i varukorg

    Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the …

  2. Mahlet G. Tadesse, Marina Vannucci - Handbook of Bayesian Variable Selection, Häftad. Tillgänglighet: Lägg i varukorg

    Handbook of Bayesian Variable Selection

    Av Mahlet G. Tadesse, Marina Vannucci

    Häftad, 2024

    968 kr

    Lägg i varukorg

    Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the …

  3. Marina Vannucci, Mahlet G. Tadesse - Handbook of Bayesian Variable Selection, E-bok. Tillgänglighet: Lägg i varukorg

    Handbook of Bayesian Variable Selection

    Av Marina Vannucci, Mahlet G. Tadesse

    E-bok, 2021

    1177 kr

    Lägg i varukorg

    Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the …

  4. Marina Vannucci, Mahlet G. Tadesse - Handbook of Bayesian Variable Selection, E-bok. Tillgänglighet: Lägg i varukorg

    Handbook of Bayesian Variable Selection

    Av Marina Vannucci, Mahlet G. Tadesse

    E-bok, 2021

    1152 kr

    Lägg i varukorg

    Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the …