We describe an efficient, exact Bayesian algorithm applicable to both variable selection and model averaging problems. A fully Bayesian approach provides a more complete characterization of the posterior ensemble of possible sub-models, but presents a computational challenge as the number of candidate variables increases. While several approximation techniques have been developed to deal with p...

Abstract This paper explores forecasting using model selection and model averaging and attempts to draw conclusion both in the context of stationarity and non-stationarity. Model averaging tends to be viewed as a polar opposite of model selection; often the motivation for averaging is to avoid the pitfalls of selecting models. However, selection cannot be avoided since every possible model cann...

Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take place are treestructures, and fixed-orderings with limited in-degree. We show how...

Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take place are treestructures, and fixed-orderings with limited in-degree. We show how...

MOTIVATION Selecting a small number of relevant genes for accurate classification of samples is essential for the development of diagnostic tests. We present the Bayesian model averaging (BMA) method for gene selection and classification of microarray data. Typical gene selection and classification procedures ignore model uncertainty and use a single set of relevant genes (model) to predict the...

Bayesian model averaging (BMA) can be seen as the optimal approach to any induction task. It can reduce error by accounting for model uncertainty in a principled way, and its usefulness in several areas has been empirically veri ed. However, few attempts to apply it to rule induction have been made. This paper reports a series of experiments designed to test the utility of BMA in this eld. BMA ...

Harmful algal blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations, and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been develop...

[1] Predictive uncertainty analysis in hydrologic modeling has become an active area of research, the goal being to generate meaningful error bounds on model predictions. State-space filtering methods, such as the ensemble Kalman filter (EnKF), have shown the most flexibility to integrate all sources of uncertainty. However, predictive uncertainty analyses are typically carried out using a sing...

This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of...