17 Gennaio 2017,
Sala Seminari, 1° piano - Dipartimento di Informatica, Sistemistica e Comunicazione, Edificio U14
Relatore/i: Dr. Marco Scutari
Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidate structures using their posterior probabilities for a given data set. Score-based algorithms then use those posterior probabilities as an objective function and return the maximum a posteriori network as the learned model. For discrete Bayesian networks, the canonical choice for a posterior score is the Bayesian Dirichlet equivalent uniform (BDeu) marginal likelihood with a uniform (U) graph prior (Heckerman et al., 1995). Its favourable theoretical properties descend from assuming a uniform prior both on the space of the network structures and on the space of the parameters of the network. In this paper, we revisit the limitations of these assumptions and we introduce an alternative set of assumptions and the resulting score: the Bayesian Dirichlet sparse (BDs) empirical Bayes marginal likelihood with a marginal uniform (MU) graph prior. We evaluate its performance in an extensive simulation study, showing that MU+BDs is more accurate than U+BDeu both in learning the structure of the network and in predicting new observations, while not being computationally more complex to estimate.
Marco studied Statistics and Computer Science at the University of Padova, Italy. He earned his Ph.D. in Statistics in Padova under the guidance of Professor A. Brogini, with a thesis on graphical modelling. He then moved to University College London (UCL) as a Research Associate in Statistical Genetics at the Genetics Institute (UGI). His research focuses on the theory of Bayesian networks and their applications to biological data, and he is the author and maintainer of the bnlearn R package.
For information contact Prof. Fabio Stella email@example.com
In archivio dal: 18/01/2017