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 limitations (e.g., instrument precision) which propagate due to the combination of variables in the function.

The uncertainty u can be expressed in a number of ways. It may be defined by the absolute error $Δ x$ . Uncertainties can also be defined by the relative error $(Δ x)/ x$ , which is usually written as a percentage. Most commonly, the uncertainty on a quantity is quantified in terms of the standard deviation, $σ$ , which is the positive square root of the variance. The value of a quantity and its error are then expressed as an interval $x \pm u$ . However, the most general way of characterizing uncertainty is by specifying its probability distribution. If the probability distribution of the variable is known or can be assumed, in theory it is possible to get any of its statistics. In particular, it is possible to derive confidence limits to describe the region within which the true value of the variable may be found. For example, the 68% confidence limits for a one-dimensional variable belonging to a normal distribution are approximately ± one standard deviation $σ$ from the central value $x$ , which means that the region $x \pm σ$ will cover the true value in roughly 68% of cases.

If the uncertainties are correlated then covariance must be taken into account. Correlation can arise from two different sources. First, the measurement errors may be correlated. Second, when the underlying values are correlated across a population, the uncertainties in the group averages will be correlated.^[1]

In a general context where a nonlinear function modifies the uncertain parameters (correlated or not), the standard tools to propagate uncertainty, and infer resulting quantity probability distribution/statistics, are sampling techniques from the Monte Carlo method family.^[2] For very expansive data or complex functions, the calculation of the error propagation may be very expansive so that a surrogate model^[3] or a parallel computing strategy^[4]^[5]^[6] may be necessary.

In some particular cases, the uncertainty propagation calculation can be done through simplistic algebraic procedures. Some of these scenarios are described below.

^ Kirchner, James. "Data Analysis Toolkit #5: Uncertainty Analysis and Error Propagation" (PDF). Berkeley Seismology Laboratory. University of California. Retrieved 22 April 2016.
^ Kroese, D. P.; Taimre, T.; Botev, Z. I. (2011). Handbook of Monte Carlo Methods. John Wiley & Sons.
^ Ranftl, Sascha; von der Linden, Wolfgang (2021-11-13). "Bayesian Surrogate Analysis and Uncertainty Propagation". Physical Sciences Forum. 3 (1): 6. arXiv:2101.04038. doi:10.3390/psf2021003006. ISSN 2673-9984.
^ Atanassova, E.; Gurov, T.; Karaivanova, A.; Ivanovska, S.; Durchova, M.; Dimitrov, D. (2016). "On the parallelization approaches for Intel MIC architecture". AIP Conference Proceedings. 1773 (1): 070001. Bibcode:2016AIPC.1773g0001A. doi:10.1063/1.4964983.
^ Cunha Jr, A.; Nasser, R.; Sampaio, R.; Lopes, H.; Breitman, K. (2014). "Uncertainty quantification through the Monte Carlo method in a cloud computing setting". Computer Physics Communications. 185 (5): 1355–1363. arXiv:2105.09512. Bibcode:2014CoPhC.185.1355C. doi:10.1016/j.cpc.2014.01.006. S2CID 32376269.
^ Lin, Y.; Wang, F.; Liu, B. (2018). "Random number generators for large-scale parallel Monte Carlo simulations on FPGA". Journal of Computational Physics. 360: 93–103. Bibcode:2018JCoPh.360...93L. doi:10.1016/j.jcp.2018.01.029.

[1] Kirchner, James. "Data Analysis Toolkit #5: Uncertainty Analysis and Error Propagation" (PDF). Berkeley Seismology Laboratory. University of California. Retrieved 22 April 2016.

[kr11-2] Kroese, D. P.; Taimre, T.; Botev, Z. I. (2011). Handbook of Monte Carlo Methods. John Wiley & Sons.

[3] Ranftl, Sascha; von der Linden, Wolfgang (2021-11-13). "Bayesian Surrogate Analysis and Uncertainty Propagation". Physical Sciences Forum. 3 (1): 6. arXiv:2101.04038. doi:10.3390/psf2021003006. ISSN 2673-9984.

[4] Atanassova, E.; Gurov, T.; Karaivanova, A.; Ivanovska, S.; Durchova, M.; Dimitrov, D. (2016). "On the parallelization approaches for Intel MIC architecture". AIP Conference Proceedings. 1773 (1): 070001. Bibcode:2016AIPC.1773g0001A. doi:10.1063/1.4964983.

[5] Cunha Jr, A.; Nasser, R.; Sampaio, R.; Lopes, H.; Breitman, K. (2014). "Uncertainty quantification through the Monte Carlo method in a cloud computing setting". Computer Physics Communications. 185 (5): 1355–1363. arXiv:2105.09512. Bibcode:2014CoPhC.185.1355C. doi:10.1016/j.cpc.2014.01.006. S2CID 32376269.

[6] Lin, Y.; Wang, F.; Liu, B. (2018). "Random number generators for large-scale parallel Monte Carlo simulations on FPGA". Journal of Computational Physics. 360: 93–103. Bibcode:2018JCoPh.360...93L. doi:10.1016/j.jcp.2018.01.029.

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