Search results
Results from the WOW.Com Content Network
There are two major types of problems in uncertainty quantification: one is the forward propagation of uncertainty (where the various sources of uncertainty are propagated through the model to predict the overall uncertainty in the system response) and the other is the inverse assessment of model uncertainty and parameter uncertainty (where the ...
Importantly the NLPD assesses the quality of the model's uncertainty quantification. It is used for both regression and classification. To compute: (1) find the probabilities given by the model to the true labels. (2) find the negative log of this product. (we actually find the negative of the sum of the logs, for numerical reasons).
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.
Experimental uncertainty analysis is a technique that analyses a derived quantity, based on the uncertainties in the experimentally measured quantities that are used in some form of mathematical relationship ("model") to calculate that derived quantity.
Quantification of Margins and Uncertainty (QMU) is a decision support methodology for complex technical decisions. QMU focuses on the identification, characterization, and analysis of performance thresholds and their associated margins for engineering systems that are evaluated under conditions of uncertainty, particularly when portions of those results are generated using computational ...
On the other hand, a negative correlation will further increase the variance of the difference, compared to the uncorrelated case. For example, the self-subtraction f = A − A has zero variance σ f 2 = 0 {\displaystyle \sigma _{f}^{2}=0} only if the variate is perfectly autocorrelated ( ρ A = 1 {\displaystyle \rho _{A}=1} ).
Identify the model output to be analysed (the target of interest should ideally have a direct relation to the problem tackled by the model). Run the model a number of times using some design of experiments, [15] dictated by the method of choice and the input uncertainty. Using the resulting model outputs, calculate the sensitivity measures of ...
They can also be used to model phenomena with significant uncertainty in inputs, such as calculating the risk of a nuclear power plant failure. Monte Carlo methods are often implemented using computer simulations, and they can provide approximate solutions to problems that are otherwise intractable or too complex to analyze mathematically.