Gross primary productivity (GPP) is the most basic variable in a carbon cycle study that determines enters ecosystem. The remote sensing-based light use efficiency (LUE) model one of tools currently used to estimate GPP at regional scale. Many models have been developed last several decades, and these well evaluated some sites. However, an accurate estimation remains challenging work using LUE ...

Abstract Postprocessing of the ensemble precipitation data improves bias and uncertainty induced in numerical weather prediction (NWP) due to perturbations initial condition atmospheric models. The evaluation NWP short-range quantitative forecasts provided by NCMRWF archived TIGGE, for Vishwamitri River Basin. aim study is perform univariate statistical postprocessing using six parametric metho...

Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts...

Prediction and assessment of water quality are important aspects resource management. To date, several index (WQI) models have been developed improved for effective However, the application these is limited because their inherent uncertainty. improve reliability WQI model quantify its uncertainty, we a WQI-Bayesian averaging (BMA) based on BMA method to merge different comprehensive groundwater...

Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA) to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts wer...

Bayesian Model Averaging (BMA), computationally feasible using Markov Chain Monte Carlo (MCMC), is a well-known method for reliable estimation of predictive distributions. The use of decision tree (DT) models for the averaging enables experts not only to estimate a predictive posterior but also to interpret models of interest and estimate the importance of predictor factors that are assumed to ...

Bayesian model averaging enables one to combine the disparate predictions of a number of models in a coherent fashion, leading to superior predictive performance. The improvement in performance arises from averaging models that make different predictions. In this work, we tap into perhaps the biggest driver of different predictions—different analysts—in order to gain the full benefits of model ...

This study performed a meta-regression analysis (MRA) to reexamine the effect of foreign direct investment (FDI) on host countries’ employment. We detected publication bias and heterogeneity between studies by employing 61 publications with 477 estimates as dataset. Studies that do not control for endogeneity suffer an upward bias. In contrast, we found downward in endogeneity. After correcting...

The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and complexity terms identifying factors that have substantial impact on Malaysian stock market has always been contentious issue. In this study, macroeconomic exchange rate, interest gold price, consumer price index, money supply M1, M2, M3, industrial product...

In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse m...