BACKGROUND Locus heterogeneity, wherein a disease can be caused in different individuals by different genes and/or environmental factors, is a ubiquitous feature of complex traits. A Bayesian approach has been proposed to account for variable rates of heterogeneity across families in a parametric linkage analysis setup [Biswas and Lin: J Am Stat Assoc 2006;101:1341-1351]. As with any parametric...

The authors introduce two ways to produce locally calibrated grid-based probabilistic forecasts of temperature. Both start from the Global Bayesian model averaging (Global BMA) statistical postprocessing method, which has constant predictive bias and variance across the domain, and modify it to make it local. The first local method, geostatistical model averaging (GMA), computes the predictive ...

Feature selection has proved to be an effective way to reduce the model complexity while giving a relatively desirable accuracy, especially, when data is scarce or the acquisition of some feature is expensive. However, the single selected model may not always generalize well for unseen test data whereas other models may perform better. Bayesian Model Averaging (BMA) is a widely used approach to...

One of the most relevant task in Sentiment Analysis is Polarity Classification. In this paper, we discuss how to explore the potential of ensembles of classifiers and propose a voting mechanism based on Bayesian Model Averaging (BMA). An important issue to be addressed when using ensemble classification is the model selection strategy. In order to help in selecting the best ensemble composition...

Although Bayesian model averaging (BMA) is in principle the optimal method for combining learned models, it has received relatively little attention in the machine learning literature. This article describes an extensive empirical study of the application of BMA to rule induction. BMA is applied to a variety of tasks and compared with more ad hoc alternatives like bagging. In each case, BMA typ...

Existing theoretical and empirical evidence on the determinants of students’ performance reveals a direct link between pre-primary education achievement test scores in primary school. Relying first-of-its-kind 2015 wave data from Programme International Student Assessment (PISA), present study analyses associations science broad set variables, including regressors that proxy education. Employin...

Multi-model averaging is currently receiving a surge of attention in the atmospheric, hydrologic, and statistical literature to explicitly handle conceptual model uncertainty in the analysis of environmental systems and derive predictive distributions of model output. Such density forecasts are necessary to help analyze which parts of the model are well resolved, and which parts are subject to ...

The mombf package implements Bayesian model selection (BMS) and model averaging (BMA) for linear, asymmetric linear, median and quantile regression. This is the main package implementing the family of non-local prior (NLP) distributions (briefly reviewed here, see Johnson and Rossell (2010, 2012) for a more detailed treatment), although other priors (mainly Zellner’s) are also implemented. The ...

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is very spread among many models suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results support this claim. In contrast with Levine and Renelt (1992), our results broadly support the more “optim...

Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for datasets where the number of variables p is large (e.g. p > 5, 000) the algorithm can become prohibitively expensive, computationally. Another method which is popular for hig...