uncertainty-toolbox/uncertainty-toolbox
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization (⭐ 1978)
Uncertainty principle (disambiguation) - Wikipedia
uncertainty principle in Wiktionary, the free dictionary. Heisenberg's uncertainty principle is a fundamental concept in quantum physics. Uncertainty
Uncertain Inferences and Uncertain Conclusions
Uncertainty may be taken to characterize inferences, their conclusions, their premises or all three. Under some treatments of uncertainty, the inferences itself is never characterized by uncertainty. We explore both the significance of uncertainty...
Survey of physics reasoning on uncertainty concepts in experiments: an assessment of measu…
Measurement uncertainty is a critical feature of experimental research in the physical sciences, and the concepts and practices surrounding measurement uncertainty are important components of physics lab courses. However, there has not been a broadly...
facebookresearch/Uncertainty-Driven-Active-Vision
Companion code for "Uncertainty-Driven Active Vision for Implicit Scene Reconstruction". This repository contains the code base for a Next Best View (NBV) task along with our proposed uncertainty driven solution and a set of baselines for comparison. (⭐ 33)
huangsg1/uncertainty-calibration
A collection of research and application papers of (uncertainty) calibration techniques. (⭐ 343)
Cone of uncertainty - Wikipedia
In project management, the cone of uncertainty describes the evolution of the amount of best case uncertainty during a project. At the beginning of a project
I am trying to accept uncertainty. So what happened to you when you accepted uncertainty?
Can you tell us how was the feelings and thoughts when you accepted it. And what did you do after accepting that uncertainty?...
Uncertainty avoidance - Wikipedia
cross-cultural psychology, uncertainty avoidance is how cultures differ on the amount of tolerance they have of unpredictability. Uncertainty avoidance is one of
CRUDE: Calibrating Regression Uncertainty Distributions Empirically
Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting. While there is extensive literature on uncertainty calibration for classification, the classif...
Uncertainty analysis - Wikipedia
Uncertainty analysis investigates the uncertainty of variables that are used in decision-making problems in which observations and models represent the
The network uncertainty quantification method for propagating uncertainties in component-b…
This work introduces the network uncertainty quantification (NetUQ) method for performing uncertainty propagation in systems composed of interconnected components. The method assumes the existence of a collection of components, each of which is chara...
A Geometric Method for Improved Uncertainty Estimation in Real-time
Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management. Post-...
Propagation of uncertainty - Wikipedia
In statistics, propagation of uncertainty is the effect of variables' uncertainties on the uncertainty of a function based on them. When the variables
Uncertainty - Wikipedia
Uncertainty or incertitude refers to situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements
Uncertainty parameter - Wikipedia
The uncertainty parameter U is introduced by the Minor Planet Center (MPC) to quantify the uncertainty of a perturbed orbital solution for a minor planet
CLOSURE: Fast Quantification of Pose Uncertainty Sets
We investigate uncertainty quantification of 6D pose estimation from learned noisy measurements (e.g. keypoints and pose hypotheses). Assuming unknown-but-bounded measurement noises, a pose uncertainty set (PURSE) is a subset of SE(3) that contains a...
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Mode…
Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We distinguish two approaches to probabilistic deep lear...
Heuristic derivation of the Casimir effect from Generalized Uncertainty Principle
After a short introduction to the generalized uncertainty principle (GUP), we discuss heuristic derivations of the Casimir effect, first from the usual Heisenberg uncertainty principle (HUP), and then from GUP. Results are compared with those obtaine...
Dynamic industry uncertainty networks and the business cycle
We argue that uncertainty network structures extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty across business cycles, we uncover a...