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Propagation of uncertainty. In statistics, propagation of uncertainty (or propagation of error) is the effect of variables ' uncertainties (or errors, more specifically random errors) on the uncertainty of a function based on them. When the variables are the values of experimental measurements they have uncertainties due to measurement ...
The GPS makes corrections for receiver clock errors and other effects but there are still residual errors which are not corrected. GPS receiver position is computed based on data received from the satellites. Errors depend on geometric dilution of precision and the sources listed in the table below.
This formula for Q arises from applying best linear unbiased estimation to a linearized version of the sensor measurement residual equations about the current solution _ = (()), except in the case of B.L.U.E. is a noise covariance matrix rather than the noise correlation matrix used in DOP, and the reason DOP makes this substitution is to ...
Uncertainty quantification. 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. An example would be to predict the acceleration ...
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Measurement uncertainty. In metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a measured quantity. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as the standard deviation.
Experimental uncertainty analysis. 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.
t. e. In machine learning, backpropagation is a gradient estimation method used to train neural network models. The gradient estimate is used by the optimization algorithm to compute the network parameter updates. It is an efficient application of the Leibniz chain rule (1673) [1] to such networks. [2]