Abstract
Graphical models are used for expressing conditional
independence relationships among variables by the
means of graphs, whose structure is typically unknown
and must be inferred by the data at hand. We propose
a theoretically sound Objective Bayes procedure
for graphical model selection. Our method is
based on the Expected-Posterior Prior and on the
Power-Expected-Posterior Prior. We use as input of the
proposed methodology a default improper prior and suggest
computationally efficient approximations of Bayes
factors and posterior odds. In a variety of simulated scenarios
with varying number of nodes and sample sizes,
we show that our method is highly competitive with,
or better than, current benchmarks. We also discuss
an application to protein-signaling data, which wieldy
confirms existing results in the scientific literature
Lingua originale | English |
---|---|
pagine (da-a) | 420-438 |
Numero di pagine | 19 |
Rivista | Statistica Neerlandica |
Volume | 74 |
DOI | |
Stato di pubblicazione | Pubblicato - 2020 |
Keywords
- Decomposable Models, Expected-Posterior Prior, FINCS, Graphical Model Selection, Objective Bayes, Power-Expected-Posterior Prior, Structure Learning