Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs

07/21/2017
by   Tatjana Pavlenko, et al.
0

In this study, we present a multi-class graphical Bayesian predictive classifier 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 uncertainty 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 Olsson et al. (2016) 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 Bayesian predictive rule that does not account for the model uncertainty, as well as to a number of out-of-the-box classifiers.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/31/2014

Marginal and simultaneous predictive classification using stratified graphical models

An inductive probabilistic classification rule must generally obey the p...
research
06/12/2018

Bayesian Inference in Nonparanormal Graphical Models

Gaussian graphical models have been used to study intrinsic dependence a...
research
12/07/2018

Rank Likelihood for Bayesian Nonparanormal Graphical Models

Gaussian graphical models, where it is assumed that the variables of int...
research
06/27/2020

Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters

This paper introduces the particle swarm algorithm, a recursive and emba...
research
02/24/2022

Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling

Chain Event Graphs (CEGs) are a widely applicable class of probabilistic...
research
02/19/2018

Heron Inference for Bayesian Graphical Models

Bayesian graphical models have been shown to be a powerful tool for disc...
research
01/23/2018

Modeling association in microbial communities with clique loglinear models

There is a growing awareness of the important roles that microbial commu...

Please sign up or login with your details

Forgot password? Click here to reset