Search results
Results from the WOW.Com Content Network
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
Conformal prediction (CP) is a machine learning framework for uncertainty quantification that produces statistically valid prediction regions (prediction intervals) for any underlying point predictor (whether statistical, machine, or deep learning) only assuming exchangeability of the data. CP works by computing nonconformity scores on ...
In machine learning and data mining, quantification (variously called learning to quantify, or supervised prevalence estimation, or class prior estimation) is the task of using supervised learning in order to train models (quantifiers) that estimate the relative frequencies (also known as prevalence values) of the classes of interest in a sample of unlabelled data items.
It is possible to select similar samples from derivative-based sensitivity through Neural Networks and perform uncertainty quantification. One advantage of the local methods is that it is possible to make a matrix to represent all the sensitivities in a system, thus providing an overview that cannot be achieved with global methods if there is a ...
A machine learning model is a type of ... functions when compared to Bayesian approaches in order to incorporate ignorance and uncertainty quantification.
In regression analysis, an interval predictor model (IPM) is an approach to regression where bounds on the function to be approximated are obtained.This differs from other techniques in machine learning, where usually one wishes to estimate point values or an entire probability distribution.
The Astroinformatics group develops new approaches to analyze and process the increasing amount of data in astronomy. The approaches of this group are based on machine/statistical learning and assist the researchers in performing the required analyses. [2] Computational Molecular Evolution (CME)
The second term is known as refinement, and it is an aggregation of resolution and uncertainty, and is related to the area under the ROC Curve. The Brier Score, and the CAL + REF decomposition, can be represented graphically through the so-called Brier Curves, [3] where the expected loss is shown for each operating condition. This makes the ...