Motivated by the identification of complex dependencies in biological networks, we present a Bayesian method for structural learning of graphical models that exhibits two distinctive features: i) it does not assume a priori an ordering of the variables, but it learns arrows when possible and lines otherwise; ii) it assumes that the observations form subgroups having different but similar structures.
Multiple arrows in the Bayesian quiver: Bayesian learning of partially directed structures from heterogeneous data / La Rocca, Luca; Castelletti, Federico; Peluso, Stefano; Stingo, Francesco Claudio; Consonni, Guido. - (2022), pp. 838-843. (Intervento presentato al convegno 51st Scientific Meeting of the Italian Statistical Society tenutosi a Caserta nel 22–24 giugno 2022).
Multiple arrows in the Bayesian quiver: Bayesian learning of partially directed structures from heterogeneous data.
La Rocca, Luca;
2022
Abstract
Motivated by the identification of complex dependencies in biological networks, we present a Bayesian method for structural learning of graphical models that exhibits two distinctive features: i) it does not assume a priori an ordering of the variables, but it learns arrows when possible and lines otherwise; ii) it assumes that the observations form subgroups having different but similar structures.File | Dimensione | Formato | |
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