Bayesian model averaging (BMA) and ensemble output statistics (EMOS) were used to improve the prediction skill of 500 hPa geopotential height field over northern hemisphere with lead times 1–7 days based on forecasts from European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers Environmental Prediction (NCEP), UK Met Office (UKMO) systems. The performance BMA EMOS compared e...

Ensembles used for probabilistic weather forecasting often exhibit a spread-skill relationship, but they tend to be underdispersive. This paper proposes a principled statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources. The BMA predictive probability density function (PDF...

We investigate the task of learning grid-based CRFs with hierarchical features motivated by the task of territory prediction in Go. We first analyze various independent and gridbased CRF classification models and state-ofthe-art training/inference algorithms to determine which offers the best performance across a variety of metrics. Faced with the performance drawbacks of independent models and...

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, t...

Mediumand long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was dev...

Currently, limited molecular markers exist that can determine where in the spectrum of chronic myeloid leukemia (CML) progression an individual patient falls at diagnosis. Gene expression profiles can predict disease and prognosis, but most widely used microarray analytical methods yield lengthy gene candidate lists that are difficult to apply clinically. Consequently, we applied a probabilisti...

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, government envisions a path to environmental sustainability and an improvement air quality. Air quality measurements were initiated different backgrounds including urban, suburban, industrial rural detect any significant changes parameters. Due dynamic nature of weather, geographical location anthropogenic sources, many u...

The deformation properties of concrete arch dams are affected by the synergistic effects multiple factors, featuring strong, multidimensional spatialtemporal evolution and distribution characteristics. This paper proposes a zoned safety monitoring model for dam based on similarity optimization to evaluate state structures. First, clustering points at different locations an was performed using p...

Accurate photovoltaic (PV) forecasting is quite crucial in planning and the regular operation of power system. Stochastic habit along with high risks PV signal uncertainty a probabilistic model required to address numerical weather prediction (NWP) underdispersion. In this study, new synthetic process based on Bayesian averaging (BMA) Ensemble Learning developed. The proposed initiated by impro...

This study analyzes the risk of severe fatal accidents within the full fossil energy chains causing five or more fatalities. The risk is quantified separately for OECD and non-OECD countries. In addition for the Coal chain, Chinese data are analyzed separately because it has been shown that data prior to 1994 were subject to strong underreporting. In order to assess the risk and its uncertainty...