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.

We studied the latent factor structure of the Beck Depression Inventory (BDI) under the light of Multidimensional Item Response Theory models. Under a Bayesian Markov chain Monte Carlo setting, we chose the most adequate model, estimated its parameters and verified its fit to the data. An evaluation of the inventory in terms of the assumed dimensions seems to agree with previous investigations in the factor structure of the BDI present in the literature. Cognitive and somatic-affective latent traits were identified in the analysis making possible the interpretation of symptom evolution along these dimensions, in terms of probability of their appearance.

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