# Propagation of uncertainty

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Propagation of uncertainty

In statistics, propagation of uncertainty (or propagation of error) is the effect of variables' uncertainties (or 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 to the combination of variables in the function.

The uncertainty is usually defined by the absolute error. Uncertainties can also be defined by the relative error Δ"x"/"x", which is usually written as a percentage.

Most commonly the error on a quantity, $Delta x$, is given as the standard deviation, $sigma$, . Standard deviation is the positive square root of variance, $sigma^2$. The value of a quantity and its error are often expressed as $xpm Delta x$. If the statistical probability distribution of the variable is known or can be assumed, 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 variable belonging to a normal distribution are ± one standard deviation from the value, that is, there is a 68% probability that the true value lies in the region $x pm sigma$.

If the variables are correlated, then covariance must be taken into account.

Linear combinations

Let $f_k\left(x_1,x_2,...,x_n\right)$ be a set of "m" functions which are linear combinations of $n$ variables $x_1,x_2,...,x_n$ with combination coefficients $A_\left\{1k\right\},A_\left\{2k\right\},...,A_\left\{nk\right\}, \left(k=1-m\right)$.:$f_k=sum_i^n A_\left\{ik\right\} x_i: mathbf \left\{f=A^Tx\right\},$ and let the variance-covariance matrix on x be denoted by $mathbf \left\{M^x\right\},$.:Then, the variance-covariance matrix $mathbf M^f,$, of "f" is given by:$M^f_\left\{ij\right\}= sum_k^n sum_l^n A_\left\{ik\right\} M^x_\left\{kl\right\} A_\left\{lj\right\}: mathbf\left\{ M^f=A^T M^x A\right\}$This is the most general expression for the propagation of error from one set of variables onto another. When the errors on "x" are un-correlated the general expression simplifies to:$M^f_\left\{ij\right\}= sum_k^n A_\left\{ik\right\} left\left(sigma^2_k ight\right)^x A_\left\{kj\right\}$Note that even though the errors on "x" may be un-correlated, their errors on "f" are always correlated. The general expressions for a single function, "f", are a little simpler.:$f=sum_i^n a_i x_i: f=mathbf \left\{a^Tx\right\},$ :$sigma^2_f= sum_i^n sum_j^n a_i M^x_\left\{ij\right\} a_j= mathbf\left\{a^T M^x a\right\}$

Each covariance term, $M_\left\{ij\right\}$ can be expressed in terms of the correlation coefficient $ho_\left\{ij\right\},$ by $M_\left\{ij\right\}= ho_\left\{ij\right\}sigma_isigma_j,$, so that an alternative expression for the variance of "f" is:$sigma^2_f= sum_i^n a_i^2sigma^2_i+sum_i^n sum_\left\{j \left(j e i\right)\right\}^n a_i a_j ho_\left\{ij\right\} sigma_isigma_j$In the case that the variables "x" are uncorrelated this simplifies further to:$sigma^2_f= sum_i^n a_i^2sigma^2_i$

Non-linear combinations

When "f" is a set of non-linear combination of the variables "x", it must usually be linearlized by approximation to a first-order Maclaurin series expansion, though in some cases, exact formulas can be derived that do not depend on the expansion.Cite journal
author = Leo Goodman
title = On the Exact Variance of Products
journal = Journal of the American Statistical Association
year = 1960
volume = 55
issue = 292
pages = 708–713
doi = 10.2307/2281592
] :$f_k approx f^0_k+ sum_i^n frac\left\{partial f_k\right\}\left\{partial \left\{x_i x_i$

where $frac\left\{partial f_k\right\}\left\{partial x_i\right\}$ denotes the partial derivative of "fk" with respect to the "i"-th variable. Since "f0k" is a constant it does not contribute to the error on "f". Therefore, the propagation of error follows the linear case, above, but replacing the linear coefficients, "Aik" and "Ajk" by the partial derivatives, $frac\left\{partial f_k\right\}\left\{partial x_i\right\}$ and $frac\left\{partial f_k\right\}\left\{partial x_j\right\}$.

Example

Any non-linear function, "f(a,b)", of two variables, "a" and "b", can be expanded as: $fapprox f^0+frac\left\{partial f\right\}\left\{partial a\right\}a+frac\left\{partial f\right\}\left\{partial b\right\}b$Whence:$sigma^2_f=left\left(frac\left\{partial f\right\}\left\{partial a\right\} ight\right)^2sigma^2_a+left\left(frac\left\{partial f\right\}\left\{partial b\right\} ight\right)^2sigma^2_b+2frac\left\{partial f\right\}\left\{partial a\right\}frac\left\{partial f\right\}\left\{partial b\right\}COV_\left\{ab\right\}$

In the particular case that $f=ab!$, $frac\left\{partial f\right\}\left\{partial a\right\}=b, frac\left\{partial f\right\}\left\{partial b\right\}=a$. Then:$sigma^2_f=b^2sigma^2_a+a^2 sigma_b^2+2abCOV_\left\{ab\right\}$or:$left\left(frac\left\{sigma_f\right\}\left\{f\right\} ight\right)^2=left\left(frac\left\{sigma_a\right\}\left\{a\right\} ight\right)^2+left\left(frac\left\{sigma_b\right\}\left\{b\right\} ight\right)^2+2left\left(frac\left\{sigma_a\right\}\left\{a\right\} ight\right)left\left(frac\left\{sigma_b\right\}\left\{b\right\} ight\right) ho_\left\{ab\right\}$

Caveats and warnings

Error estimates for non-linear functions are biased on account of using a truncated series expansion. The extent of this bias depends on the nature of the function. For example, the bias on the error calculated for log x increases as x increases since the expansion to 1+x is a good approximation only when x is small.

In data-fitting applications it is often possible to assume that measurements errors are uncorrelated. Nevertheless, parameters derived from these measurements, such as least-squares parameters, will be correlated. For example, in linear regression, the errors on slope and intercept will be correlated and the term with the correlation coefficient, &rho;, can make a significant contribution to the error on a calculated value.:$y=mz+c: sigma^2_y=z^2sigma^2_m+sigma^2_c+2z ho sigma_msigma_c$

In the special case of the inverse $1/B$ where $B=N\left(0,1\right)$, the distribution is a Cauchy distribution and there is no definable variance. For such ratio distributions, there can be defined probabilities for intervals which can be defined either by Monte Carlo simulation, or, in some cases, by using the Geary-Hinkley transformation.Cite journal
author = Jack Hayya, Donald Armstrong and Nicolas Gressis
title = A Note on the Ratio of Two Normally Distributed Variables
journal = Management Science
year = 1975
volume = 21
issue = 11
pages = 1338–1341
month = July
]

Example formulas

This table shows the variances of simple functions of the real variables $A,B,$ with standard deviations $sigma_A, sigma_B,$, and precisely-known real-valued constants $a,b,$.

:For uncorrelated variables the covariance terms are zero.Expressions for more complicated functions can be derived by combining simpler functions. For example, repeated multiplication, assuming no correlation gives,:$f = AB\left(C\right): left\left(frac\left\{sigma_f\right\}\left\{f\right\} ight\right)^2 = left\left(frac\left\{sigma_A\right\}\left\{A\right\} ight\right)^2 + left\left(frac\left\{sigma_B\right\}\left\{B\right\} ight\right)^2+ left\left(frac\left\{sigma_C\right\}\left\{C\right\} ight\right)^2$

Partial derivatives

Given $X=f\left(A, B, C, cdots\right)$:

Example calculation: Inverse tangent function

We can calculate the uncertainty propagation for the inverse tangent function as an example of using partial derivatives to propagate error.

Define

:$f\left( heta\right) = arctan\left\{ heta\right\}$,

where $sigma_\left\{ heta\right\}$ is the absolute uncertainty on our measurement of $heta$.

The partial derivative of $f\left( heta\right)$ with respect to $heta$ is

:$frac\left\{partial f\right\}\left\{partial heta\right\} = frac\left\{1\right\}\left\{1+ heta^2\right\}$.

Therefore, our propagated uncertainty is

:$sigma_\left\{f\right\} = frac\left\{sigma_\left\{ heta\left\{1+ heta^2\right\}$,

where $sigma_\left\{f\right\}$ is the absolute propagated uncertainty.

Example application: Resistance measurement

A practical application is an experiment in which one measures current, "I", and voltage, "V", on a resistor in order to determine the resistance, "R", using Ohm's law, $R = V / I.$

Given the measured variables with uncertainties, "I"±Δ"I" and "V"±Δ"V", the uncertainty in the computed quantity, Δ"R" is

: $Delta R = left\left( left\left(frac\left\{Delta V\right\}\left\{I\right\} ight\right)^2+left\left(frac\left\{V\right\}\left\{I^2\right\}Delta I ight\right)^2 ight\right)^\left\{1/2\right\} = Rsqrt\left\{left\left(frac\left\{Delta V\right\}\left\{V\right\} ight\right)^2+left\left(frac\left\{Delta I\right\}\left\{I\right\} ight\right)^2\right\}.$

Thus, in this simple case, the relative error Δ"R"/"R" is simply the square root of the sum of the squares of the two relative errors of the measured variables.

ee also

* Errors and residuals in statistics
* Accuracy and precision
* Delta method
* Significance arithmetic
* Automatic differentiation

Notes

* [http://physicslabs.phys.cwru.edu/MECH/Manual/Appendix_V_Error%20Prop.pdf Uncertainties and Error Propagation] , Appendix V from the Mechanics Lab Manual, Case Western Reserve University.
* [http://www.av8n.com/physics/uncertainty.htm A detailed discussion of measurements and the propagation of uncertainty] explaining the benefits of using error propagation formulas and monte carlo simulations instead of simple significance arithmetic.

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