Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such nature in ANNs causes the limitations of using for medical diagnosis, law problems, and portfolio management which not only discovering but also uncertainty essentially required. In order to address such problem, we propose probabilistic ...

We present a method for image-based crowd counting, one that can predict density map together with the uncertainty values pertaining to predicted map. To obtain prediction uncertainty, we model using Gaussian distributions and develop convolutional neural network architecture these distributions. A key advantage of our over existing counting methods is its ability quantify predictions. illustra...

Definitions of the concepts of bias and recovery are discussed and approaches to dealing with them described. The Guide To Uncertainty in Measurement (GUM) recommends correction for all significant systematic effects, but it is also possible to expand measurement uncertainty to take account of uncorrected bias. Run, laboratory and method bias can be defined as components of the bias of a partic...

This paper presents a mathematical approach to properly account for uncertainty in wind resource assessment and wind energy production estimation. The evaluation of a wind resource and the subsequent estimation of the annual energy production (AEP) is a highly uncertain process. Uncertainty arises at all points in the process, from measuring the wind speed to the uncertainty in a power curve. A...

Quantifying parameter and prediction uncertainty in a rigorous framework can be an important component of model skill assessment. Generally, models with lower uncertainty will be more useful for prediction and inference than models with higher uncertainty. Ensemble estimation, an idea with deep roots in the Bayesian literature, can be useful to reduce model uncertainty. It is based on the idea ...

Machine Translation Quality Estimation is a notoriously difficult task, which lessens its usefulness in real-world translation environments. Such scenarios can be improved if quality predictions are accompanied by a measure of uncertainty. However, models in this task are traditionally evaluated only in terms of point estimate metrics, which do not take prediction uncertainty into account. We i...

many constants and coefficient effect on the modeling results. in many cases, it is possible these constants and coefficients differ from their real values in the environment, but the modeling results adapted with real boundary conditions due to other factors adjustment during modeling. uncertainty analysis is a tool for assessing the effect of uncertainty of various factors on the modeling res...