Bissiri et al. (2016) present a general Bayesian approach where the like- lihood is replaced more generally by a loss function, which is used to derive the posterior distribution from the prior. In this paper, we exploit this idea for learning the structure of undirected graphical models over discrete variables.

Bayesian Inference of Undirected Graphical Models from Count Data

Pier Giovanni Bissiri;Monica Chiogna;
2020

Abstract

Bissiri et al. (2016) present a general Bayesian approach where the like- lihood is replaced more generally by a loss function, which is used to derive the posterior distribution from the prior. In this paper, we exploit this idea for learning the structure of undirected graphical models over discrete variables.
Book of short papers SIS 2020
638
643
Pier Giovanni Bissiri, Monica Chiogna, Nguyen Thi Kim Hue
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/779975
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