This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model (BMA) reliability ensemble (REA), in post-processing runoff projections derived from coupled hydrological models climate downscaling models. The performance weight distributions five ensembles were thoroughly compared, including simple equal-weight averaging, BMA, REAs optimiz...

In modelling we usually endeavour to find a single 'best' model that explains the relationship between independent and dependent variables. Selection of a single model fails to take into account the prior uncertainty in the model space. The Bayesian model averaging (BMA) approach tackles this problem by considering the set of all possible models. We apply BMA approach to the estimation of the f...

Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, dis...

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fit...

Abstract Determining groundwater potential is vital for resource management. This study aims to present a comparative analysis of three widely used ensemble techniques (averaging, bagging, and boosting) in spring mapping. Firstly, 12 spring-related factors total 79 locations were collected as the dataset. Secondly, typical models adopted predict potential, namely, Bayesian model averaging (BMA)...

Bayesian model averaging (BMA) has recently been proposed as a statistical method to calibrate forecast ensembles from numerical weather models. Successful implementation of BMA however, requires accurate estimates of the weights and variances of the individual competing models in the ensemble. In their seminal paper (Raftery et al. Mon Weather Rev 133:1155–1174, 2005) has recommended the Expec...

Abstract This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble postprocessing via Bayesian model averaging (BMA), and ProbForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the compo...

We explored the relationship between 24 character strengths measured by Global Assessment of Character Strengths (GACS), which was revised from original VIA instrument, and moral functioning comprising postconventional reasoning, empathic traits identity. Bayesian Model Averaging (BMA) employed to explore best models, were more parsimonious than full regression models estimated through frequent...

Accurate weather forecasts benefit society in crucial functions, including agriculture, transportation, recreation, and basic human and infrastructural safety. Over the past two decades, ensembles of numerical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate probabilistic forecasts for future weather events. H...