While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains challenging task. To address this challenge, we propose implement loss function for regression estimation based on the Bayesian Validation Metric (BVM) framework while using ensemble learning. The proposed reproduces maximum likelihood limiting ...

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model choice and strong baseline for segmentation. However, despite its extraordinary performance, does not supply measure uncertainty to indicate possible failure. This can be problematic large-scale applications, where data are heterogeneous m...

In this paper an efficient three- stage algorithm is developed for software production cost and time estimation. First stage includes a hybrid model composed of COCOMO and Function Points methods to increase estimation accuracy. Second stage encompasses paired comparisons matrix of analytical hierarchy process to determine amount of any resources consumed in each step of software production by ...

Reference Tomm a s Svoboda and Peter Sturm. A badly calibrated camera in ego-motion estimation, propagation of uncertainty. Abstract. This paper deals with the ego-motion estimation (motion of the camera) from two views. To estimate an ego-motion we have to nd correspondences and we need a calibrated camera. In this paper we solve the problem how to propagate known camera calibration errors int...

Position-momentum uncertainty relation is examined in the light of quantum estimation theory, and some counterintuitive results are obtained. One important conclusion is that the conclusion that the so-called `minimum-uncertainty state' is actually is the maximal uncertainty state.

In many applications of the neural networks, predicting the conditional average of the target variable is not sufficient. Often, real life problems also require estimation of the uncertainty. In this study, uncertainty analysis is applied on a remote sensing problem of Aerosol Optical Depth (AOD) estimation. AOD is one of the most important properties of the atmosphere that indicates the amount...

uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. the objective of this study is to develop and apply a bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in zidasht catchment, iran....

Robust Kalman filtering problems for discrete-time Markovian jump systems with parameter and noise uncertainty were investigated. Because of the existence of stochastic Markovian switching, the covariance matrices of system state noise and observation noise are time-varying or unmeasurable instead of stationary, meanwhile the system suffers from structure parameter uncertainty as well. By view ...

The majority of uncertainty factors that caused major problems were operational in the sense that they invoked problems that are normally within the responsibility of the Software Project Management. Each project will have a different capacity for uncertainty. This is a measure of how much uncertainty the project can tolerate. Accurate software cost and schedule estimations are essential especi...

This paper considers the joint problem of model estimation and implementation of monetary policy in the face of uncertainty regarding the process of structural change in the economy. We model unobserved structural change through time variation in the natural rates of interest and unemployment. We show that certainty equivalent optimal policies perform poorly when there is model uncertainty abou...