Bayesian model averaging: A systematic review and conceptual classification

TL;DR

This paper systematically reviews 587 articles on Bayesian Model Averaging from 1996 to 2014, classifies the literature to understand trends, and discusses future research directions in the field.

Contribution

It provides a comprehensive classification scheme and updated analysis of BMA literature, highlighting developments and guiding future research.

Findings

Identified key trends and assumptions in BMA applications

Developed a conceptual classification scheme for BMA literature

Discussed future directions and research gaps in BMA

Abstract

Bayesian Model Averaging (BMA) is an application of Bayesian inference to the problems of model selection, combined estimation and prediction that produces a straightforward model choice criteria and less risky predictions. However, the application of BMA is not always straightforward, leading to diverse assumptions and situational choices on its different aspects. Despite the widespread application of BMA in the literature, there were not many accounts of these differences and trends besides a few landmark revisions in the late 1990s and early 2000s, therefore not taking into account any advancements made in the last 15 years. In this work, we present an account of these developments through a careful content analysis of 587 articles in BMA published between 1996 and 2014. We also develop a conceptual classification scheme to better describe this vast literature, understand its trends and future directions and provide guidance for the researcher interested in both the application and development of the methodology. The results of the classification scheme and content review are then used to discuss the present and future of the BMA literature.