Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs

In this study, we present a multi-class graphical Bayesian predictive classi- fier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. For each class, the dependence structure underlying the observed features is represented by a set of decomposable Gaussian graphical models. Emphasis is then placed on the Bayesian model averaging which takes full account of the class-specific model un- certainty by averaging over the posterior graph model probabilities. Even though the decomposability assumption severely reduces the model space, the size of the class of decomposable models is still immense, rendering the explicit Bayesian averaging over all the models infeasible. To address this issue, we consider the particle Gibbs strategy of (second paper) for posterior sampling from decomposable graphical models which utilizes the Christmas tree algorithm of Rios et al. (2016) as proposal kernel. We also derive the a strong hyper Markov law which we call the hyper normal Wishart law that allow to perform the resultant Bayesian calculations locally. The proposed predictive graphical classifier reveals superior performance compared to the ordinary (non-averaged) Bayesian predictive rule (that does not account for the model uncertainty), as well as to a number of out-of-the-box classifiers.