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.File in questo prodotto:
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