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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 ...
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.
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]
The centenary event organised by the University of Oxford and supported by The Alan Turing Institute for the Treatise and Frank Knight's Risk, Uncertainty, and Profit noted: [81] In Risk, Uncertainty, and Profit, Knight put forward the vital difference between risk, where empirical evaluation of unknown outcomes can still be applicable, and ...
Intuitively, the entropy H X of a discrete random variable X is a measure of the amount of uncertainty associated with the value of X when only its distribution is known. The entropy of a source that emits a sequence of N symbols that are independent and identically distributed (iid) is N ⋅ H bits (per message of N symbols).
In physical experiments uncertainty analysis, or experimental uncertainty assessment, deals with assessing the uncertainty in a measurement.An experiment designed to determine an effect, demonstrate a law, or estimate the numerical value of a physical variable will be affected by errors due to instrumentation, methodology, presence of confounding effects and so on.
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Michael Osborne (born 1982 [1]) is an Australian academic and scientist who serves as a professor of machine learning at University of Oxford in the Machine Learning Research Group in the Department of Engineering Science. [2] In 2016 he co-founded Mind Foundry, [3] an artificial intelligence company, along with fellow professor Stephen Roberts ...