Water utilities often rely on water main failure prediction model for developing preventive or proactive repair and replacement action program. Due to inherent uncertainties in modeling, it is challenging to understand the water main failure processes and to predict the failure effectively. In this study, Bayesian model averaging (BMA) method is presented to identify the influential covariates ...

In this article, we are concerned with tracking an object of interest in video stream. We propose an algorithm that is robust against occlusion, the presence of confusing colors, abrupt changes in the object feature space and changes in object size. We develop the algorithm within a Bayesian modeling framework. The state space model is used for capturing the temporal correlation in the sequence...

OBJECTIVE To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. DESIGN A time-series study using regional death registry between 2009 and 2010. SETTING 8 districts in a large metr...

Bayesian model averaging (BMA) is the state of the art approach for overcoming model uncertainty. Yet, especially on small data sets, the results yielded by BMA might be sensitive to the prior over the models. Credal Model Averaging (CMA) addresses this problem by substituting the single prior over the models by a set of priors (credal set). Such approach solves the problem of how to choose the...

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For (potentially misspecified) stand-alone model, it provides reliable price of risk estimates both tradable nontradable factors, detects those weakly identified. competing factors (possibly nonnested) models, the method automatically selects best specification—if...

Hjort and Claeskens (HC) argue that statistical inference conditional on a single selected model underestimates uncertainty, and that model averaging is the way to remedy this; we strongly agree. They point out that Bayesian model averaging (BMA) has been the dominant approach to this, but argue that its performance has been inadequately studied, and propose an alternative, Frequentist Model Av...

Stand growth-and-yield models include whole-stand models, individual-tree models, and diameter distribution models. Based on the growth data of Chinese fir (Cunninghamia lanceolata [Lamb.] Hook.) in Fenyi County, Jiangxi Province, in southern China, Bayesian model averaging (BMA) was used to forecast stand basal areas by combining these three types of models into a single predictive model. BMA ...

Gene expression microarray data has recently become a popular method for classification in a variety of diagnostic areas. Classification is the prediction of the diagnostic category of a tissue sample from its expression array phenotype given the availability of similar data from tissues in identified categories. A challenge in predicting diagnostic categories using microarray data is that the ...

Although some previous studies have investigated the relationship between moral foundations and judgment development, methods used not been able to fully explore relationship. In present study, we Bayesian Model Averaging (BMA) in order address limitations traditional regression that previously. Results showed consistency with findings binding are negatively correlated post-conventional reasoni...

Model uncertainty is a pervasive problem in regression applications. Bayesian model averaging (BMA) takes into account and identifies robust determinants. However, it requires the specification of suitable priors. Mixture priors are appealing because they explicitly for different groups covariates as Specific Dirichlet process clustering (DPC) proposed; their correspondence to binomial prior de...