Fact-checked by Grok 2 weeks ago

Propagation of uncertainty

Propagation of uncertainty, also known as error propagation, refers to the techniques used to quantify how uncertainties in measured input variables contribute to the overall in a computed output derived from those variables through a mathematical . This process is fundamental in measurement science, ensuring that the reliability of results from experiments and calculations is properly assessed by propagating input errors to the final outcome. The cornerstone of propagation of uncertainty is the law of propagation of uncertainty, which provides an analytical method to estimate the combined standard uncertainty u_c(y) for a quantity y = f(x_1, x_2, \dots, x_N), where x_i are input quantities with standard uncertainties u(x_i). The formula is given by: u_c^2(y) = \sum_{i=1}^N \left( \frac{\partial f}{\partial x_i} \right)^2 u^2(x_i) + 2 \sum_{i=1}^{N-1} \sum_{j=i+1}^N \frac{\partial f}{\partial x_i} \frac{\partial f}{\partial x_j} u(x_i, x_j), where \frac{\partial f}{\partial x_i} are sensitivity coefficients representing partial derivatives evaluated at the best estimates of the inputs, and u(x_i, x_j) are covariances accounting for dependencies between variables. When input quantities are independent (uncorrelated), the covariance terms vanish, simplifying the expression to a root-sum-of-squares form that combines variances quadratically. This law relies on a first-order approximation, assuming uncertainties are small relative to the values of the inputs and that the function is sufficiently linear in the vicinity of those values; for larger uncertainties or highly nonlinear functions, higher-order expansions or alternative approaches may be necessary. Simplified rules apply to common operations: for or (z = x \pm y), the absolute uncertainties combine as u(z) = \sqrt{u^2(x) + u^2(y)}; for or (z = x \times y or z = x / y), the relative uncertainties add in quadrature, \left( \frac{u(z)}{|z|} \right)^2 = \left( \frac{u(x)}{|x|} \right)^2 + \left( \frac{u(y)}{|y|} \right)^2; and for powers (z = x^k), the relative uncertainty is \frac{u(z)}{|z|} = |k| \frac{u(x)}{|x|}. In addition to analytical methods, numerical techniques such as simulation propagate entire probability distributions of inputs through the function to obtain the output distribution, providing a more robust assessment for complex or nonlinear models without relying on approximations. These approaches are standardized in documents like the Joint Committee for Guides in (JCGM) Guide to the Expression of in (), which emphasizes identifying all uncertainty sources, including Type A (statistical) and Type B (other), before propagation. Propagation of uncertainty is applied across disciplines, from physics and to and , to ensure traceable and reliable measurements; for instance, in force measurement systems, it helps evaluate how input uncertainties affect output accuracy. Failure to properly propagate uncertainties can lead to overconfidence in results, underscoring its role in scientific validity and .

Fundamentals

Definition and Scope

Propagation of uncertainty, also known as error propagation, is the process of determining how uncertainties—typically quantified as standard deviations or variances—in input variables influence the uncertainty in the output of a that combines those inputs. This quantification ensures that the reliability of derived results reflects the limitations inherent in the measured or estimated inputs, providing a complete of outcomes in scientific and technical contexts. The origins of uncertainty propagation trace back to 19th-century physics, where developed foundational principles for propagating errors in linear combinations of observations as part of his work on estimation, detailed in his 1823 publication Theoria Combinationis Observationum Erroribus Minimis Obnoxiae. These early ideas were extended and formalized in statistical throughout the 20th , with significant advancements in leading to standardized approaches, such as the 1993 Guide to the Expression of Uncertainty in Measurement (GUM) issued by the (ISO). Assessing propagated uncertainty is crucial for evaluating the trustworthiness of experimental measurements, computational simulations, and theoretical models across disciplines like physics, , and chemistry, as it allows researchers to express results with appropriate levels and avoid overconfidence in precise but potentially unreliable outputs. While propagation methods focus primarily on random s evaluated through statistical analysis, systematic errors—arising from biases or unaccounted influences—are addressed separately by incorporating them as additional uncertainty contributions to ensure comprehensive error assessment. The scope of uncertainty propagation includes analytical techniques that use mathematical formulas to approximate output uncertainties and numerical approaches like methods for handling complex, non-analytical functions. These methods generally presume independence among input variables, though extensions account for dependencies via terms when relevant. Approaches vary from linear methods suitable for small perturbations to nonlinear ones for broader applicability, as explored in later sections.

Notation and Basic Principles

In the context of uncertainty propagation, input quantities are typically denoted as X_i for i = 1, 2, \dots, n, where each X_i has an estimate x_i and an associated standard uncertainty u(x_i), representing the standard deviation of X_i. The output quantity, or measurand, is expressed as Y = f(X_1, X_2, \dots, X_n), with its estimate given by y = f(x_1, x_2, \dots, x_n). Partial derivatives \frac{\partial f}{\partial x_i}, evaluated at the input estimates, serve as sensitivity coefficients that quantify how changes in each input affect the output. Fundamental assumptions underpin these methods, including that uncertainties are small relative to the magnitudes of the variables, justifying a first-order expansion of the function f. Errors are commonly modeled as following a Gaussian (normal) distribution, with the standard uncertainty corresponding to one standard deviation. When uncertainties are larger, higher-order terms in the expansion may be necessary to achieve adequate accuracy. Propagation formulas are formulated in terms of variances u^2(x_i) rather than standard deviations u(x_i), as variances are additive for uncorrelated inputs and readily incorporate correlations via terms. This approach yields the combined standard uncertainty u_c(y) for the output through the law of propagation of uncertainty. Regarding probability distributions, propagation techniques estimate the mean of Y as approximately f applied to the input means, while the variance of Y follows from the additivity of variances under the specified approximations, assuming or known dependencies among inputs.

Linear Propagation

Sums and Differences

In the context of uncertainty propagation, the variance of a of random variables is the of their individual variances. For a y = x_1 + x_2 + \dots + x_n, where each x_i is an random variable with \mu_{x_i} and variance \sigma_{x_i}^2, the variance of y is given by \sigma_y^2 = \sigma_{x_1}^2 + \sigma_{x_2}^2 + \dots + \sigma_{x_n}^2. This formula arises directly from the additivity property of variances for variables. Similarly, for a y = x_1 - x_2, where x_1 and x_2 are , the variance is \sigma_y^2 = \sigma_{x_1}^2 + \sigma_{x_2}^2. This result follows because can be expressed as with a negative , and the variance of a negated equals the original variance. The derivation of these formulas relies on the definition of variance and the properties of and . The variance of y = \sum_{i=1}^n x_i is \sigma_y^2 = \mathbb{E}\left[ \left( \sum_{i=1}^n (x_i - \mu_{x_i}) \right)^2 \right] = \sum_{i=1}^n \sum_{j=1}^n \mathbb{E}\left[ (x_i - \mu_{x_i})(x_j - \mu_{x_j}) \right] = \sum_{i=1}^n \sigma_{x_i}^2, since the cross terms vanish for (i.e., \mathrm{Cov}(x_i, x_j) = 0 for i \neq j). For the , the same yields \sigma_y^2 = \sigma_{x_1}^2 + \sigma_{x_2}^2 - 2\mathrm{Cov}(x_1, x_2), which simplifies under . This approach extends naturally to weighted linear combinations, where y = \sum_{i=1}^n a_i x_i and the a_i are constants. The variance then becomes \sigma_y^2 = \sum_{i=1}^n a_i^2 \sigma_{x_i}^2, assuming among the x_i. This follows from the of and the scaling property of variance, where \mathrm{Var}(a_i x_i) = a_i^2 \sigma_{x_i}^2, with additivity applying to the weighted terms.

Products and Quotients

In the context of linear propagation of , products and quotients of measured quantities are handled by propagating relative uncertainties, which are the deviations divided by the values of the inputs. This approach is particularly useful when the uncertainties are small compared to the values themselves, allowing for an based on the Taylor expansion. For a product y = x_1 x_2 \dots x_n, where each x_i has an associated uncertainty \sigma_{x_i}, the relative uncertainty in y is approximated as the quadrature sum of the individual relative uncertainties: \frac{\sigma_y}{y} \approx \sqrt{ \sum_{i=1}^n \left( \frac{\sigma_{x_i}}{x_i} \right)^2 } This formula assumes the input uncertainties are independent and follows from the general law of propagation of uncertainty in the Guide to the Expression of Uncertainty in Measurement (GUM). Similarly, for a quotient y = \frac{x_1}{x_2}, the relative uncertainty is given by \frac{\sigma_y}{y} \approx \sqrt{ \left( \frac{\sigma_{x_1}}{x_1} \right)^2 + \left( \frac{\sigma_{x_2}}{x_2} \right)^2 }, which extends analogously to more variables in the numerator and denominator. This mirrors the product case since division is multiplication by the reciprocal. The derivation of these approximations can be obtained through logarithmic differentiation. For the product y = \prod x_i, taking the natural logarithm yields \ln y = \sum \ln x_i, and differentiating gives \frac{dy}{y} = \sum \frac{dx_i}{x_i}. Assuming small, independent errors and using the root-sum-square for variances, the relative variance follows as \left( \frac{\sigma_y}{y} \right)^2 \approx \sum \left( \frac{\sigma_{x_i}}{x_i} \right)^2. Alternatively, the first-order Taylor expansion of y around the mean values directly leads to the same variance expression via partial derivatives. For the first-order approximation including the covariance term in the case of two variables, such as y = x_1 x_2, the variance is \sigma_y^2 = \mu_y^2 \left( \frac{\sigma_{x_1}^2}{\mu_{x_1}^2} + \frac{\sigma_{x_2}^2}{\mu_{x_2}^2} + 2 \frac{\Cov(x_1, x_2)}{\mu_{x_1} \mu_{x_2}} \right), where \mu denotes means; however, detailed treatment of covariances is addressed elsewhere. The approximate forms neglect this term, valid when inputs are uncorrelated.

Nonlinear Propagation

General Taylor Expansion Method

The general Taylor expansion method provides an analytical framework for estimating the propagated in the output of a nonlinear based on the uncertainties in its inputs. This technique approximates the function using a multivariate expansion centered at the expected values of the inputs, typically employing a to linearize the problem and compute the output variance via the law of propagation of uncertainty. The method is widely adopted in , , and for its mathematical rigor and computational tractability, forming the basis of the standard approach outlined in guidelines. To derive the uncertainty propagation formula, consider a scalar-valued function y = f(\mathbf{x}), where \mathbf{x} = (x_1, x_2, \dots, x_n)^T is a vector of input random variables with expected values \boldsymbol{\mu} = E[\mathbf{x}] and \boldsymbol{\Sigma}. The first-order expansion of f around \boldsymbol{\mu} is f(\mathbf{x}) \approx f(\boldsymbol{\mu}) + \sum_{i=1}^n \frac{\partial f}{\partial x_i} \bigg|_{\boldsymbol{\mu}} (x_i - \mu_i), neglecting higher-order terms. The expected value of the output is then E \approx f(\boldsymbol{\mu}), and the variance \sigma_y^2 = \Var(y) approximates to the variance of the linear form, yielding \sigma_y^2 \approx \sum_{i=1}^n \left( \frac{\partial f}{\partial x_i} \bigg|_{\boldsymbol{\mu}} \right)^2 \sigma_{x_i}^2 + 2 \sum_{1 \leq i < j \leq n} \frac{\partial f}{\partial x_i} \bigg|_{\boldsymbol{\mu}} \frac{\partial f}{\partial x_j} \bigg|_{\boldsymbol{\mu}} \Cov(x_i, x_j). This expression accounts for both the individual variances \sigma_{x_i}^2 and the covariances between inputs, with all partial derivatives evaluated at the point \boldsymbol{\mu}. In the multivariate case, where the output is a vector \mathbf{y} = \mathbf{f}(\mathbf{x}) with m components, the approximation extends naturally using matrix notation. The Jacobian matrix \mathbf{J} is defined with elements J_{k,i} = \partial f_k / \partial x_i \big|_{\boldsymbol{\mu}} for k = 1, \dots, m and i = 1, \dots, n. The covariance matrix of the output \boldsymbol{\Sigma}_y is then given by \boldsymbol{\Sigma}_y \approx \mathbf{J} \boldsymbol{\Sigma} \mathbf{J}^T, which generalizes the scalar variance formula and facilitates efficient computation, especially in software implementations for complex models. For the scalar case, this reduces to \sigma_y^2 = \nabla f(\boldsymbol{\mu})^T \boldsymbol{\Sigma} \nabla f(\boldsymbol{\mu}), where \nabla f is the gradient vector. The derivation relies on key assumptions: the input uncertainties must be sufficiently small such that the higher-order terms in the Taylor series—arising from second and subsequent derivatives—contribute negligibly to the variance. These second-order terms, involving the Hessian matrix \mathbf{H} of second partial derivatives, introduce corrections like \frac{1}{2} \trace(\mathbf{H} \boldsymbol{\Sigma}) to the mean and additional variance components, but they are omitted in the standard first-order method to maintain linearity. All derivatives are evaluated at the expected values \boldsymbol{\mu} to ensure the approximation is centered appropriately, and the inputs are assumed to follow distributions where moments up to the second order are well-defined. When these conditions hold, the method provides a reliable estimate; otherwise, higher-order expansions or numerical alternatives may be necessary.

Approximations for Small Uncertainties

When uncertainties in the input variables are small relative to their values but the function relating them to the output is nonlinear, first-order approximations may introduce noticeable bias or underestimate the spread. In such cases, practical simplifications and bounds extend the linear propagation framework without requiring full numerical methods. These approximations provide conservative estimates or refined corrections, particularly useful in engineering and metrology where rapid assessments are needed. One common bound is the maximum error propagation, which assumes the worst-case scenario where all contributions to the error align in the same direction. For a function y = f(\mathbf{x}), the maximum possible uncertainty in y is given by \sigma_y \leq \sum_i \left| \frac{\partial f}{\partial x_i} \right| \sigma_{x_i}, evaluated at the nominal values of \mathbf{x}. This linear bound overestimates the typical uncertainty compared to the root-sum-square approach but guarantees that the true error does not exceed it, making it suitable for safety-critical applications. For improved accuracy, a second-order approximation incorporates curvature effects from the Hessian matrix of the function. The expected value of y receives a correction term \frac{1}{2} \sum_{i,j} \frac{\partial^2 f}{\partial x_i \partial x_j} \Cov(x_i, x_j), while the variance includes additional contributions from second derivatives and higher moments of the inputs. This refines the first-order estimate by accounting for nonlinearity, reducing bias in the propagated uncertainty when partial second derivatives are computable. Software tools can automate these calculations using symbolic differentiation. For functions involving positive variables where relative uncertainties are prominent, a logarithmic approximation transforms the problem to additive errors in log space. If y = f(x) with x > 0, the relative uncertainty in y approximates \sigma_y / |y| \approx \left| \frac{\partial \ln f}{\partial \ln x} \right| (\sigma_x / |x|), leveraging the fact that logarithms linearize multiplicative effects. This is particularly effective for ratios or exponentials, simplifying propagation for quantities like concentrations or gains. These approximations are valid when the relative uncertainties satisfy \sigma_{x_i} / |x_i| < 0.1 (or 10%), ensuring higher-order terms remain negligible compared to the first-order contributions. Beyond this threshold, the linearization error becomes significant, necessitating higher-order or simulation-based methods. Validation involves comparing against Monte Carlo results for the specific function.

Correlations and Dependencies

Incorporating Covariances

In uncertainty propagation, dependencies between input variables must be accounted for through their covariances, which quantify how variations in one variable relate to variations in another. The covariance between two variables x_i and x_j is defined as \operatorname{Cov}(x_i, x_j) = \sigma_{x_i x_j}, representing the expected value of the product of their deviations from their means. This measure captures both the direction and strength of the linear relationship; a positive covariance indicates that the variables tend to deviate in the same direction, while a negative value indicates opposite deviations. To normalize covariance and obtain a dimensionless quantity bounded between -1 and 1, the correlation coefficient is introduced: \rho_{ij} = \frac{\operatorname{Cov}(x_i, x_j)}{\sigma_{x_i} \sigma_{x_j}}, where \sigma_{x_i} and \sigma_{x_j} are the standard uncertainties (standard deviations) of x_i and x_j, respectively. A value of \rho_{ij} = 1 signifies perfect positive linear correlation, \rho_{ij} = 0 indicates no linear correlation, and \rho_{ij} = -1 denotes perfect negative correlation. When propagating uncertainty for a function y = f(x_1, x_2, \dots, x_n) of multiple correlated inputs, the variance of y is modified from the independent case to include cross terms. The general formula for the propagated variance, derived from a first-order Taylor expansion, is \sigma_y^2 = \sum_i \left( \frac{\partial f}{\partial x_i} \sigma_{x_i} \right)^2 + 2 \sum_{i < j} \frac{\partial f}{\partial x_i} \frac{\partial f}{\partial x_j} \operatorname{Cov}(x_i, x_j), where the partial derivatives are evaluated at the best estimates of the inputs. This expression reduces to the independent case when all covariances are zero (\operatorname{Cov}(x_i, x_j) = 0 for i \neq j). The covariance terms can either increase or decrease the overall uncertainty depending on the signs of the partial derivatives and the covariances; for instance, negative correlations between variables with opposite sensitivities can reduce \sigma_y. For a more compact and general representation, especially with multiple outputs or higher dimensions, the covariance matrix form is used. Let \Sigma_x be the covariance matrix of the input vector \mathbf{x}, with elements \Sigma_{x,ij} = \operatorname{Cov}(x_i, x_j), and \mathbf{J} the Jacobian matrix with elements J_{ki} = \frac{\partial f_k}{\partial x_i} for output functions f_k. The propagated covariance matrix for the outputs \mathbf{y} is then \Sigma_y = \mathbf{J} \Sigma_x \mathbf{J}^T. This matrix form facilitates computations in vectorized software and extends naturally to multivariate cases. Correlations in input uncertainties arise from various sources in measurement and modeling contexts. Measurement errors often introduce covariances when multiple quantities are affected by shared influences, such as calibration offsets from the same instrument or environmental factors like temperature fluctuations impacting simultaneous readings. In model-based evaluations, dependencies can stem from assumptions where variables are not truly independent, for example, when parameters in a physical model are estimated from the same dataset, leading to correlated estimates. Recognizing and estimating these covariances is essential, typically through repeated measurements or prior knowledge of the error structure, to ensure accurate propagation.

Impact on Variance Calculations

When correlations between input variables are present, they significantly alter the propagated variance in calculated quantities, often leading to either amplification or reduction of uncertainty depending on the sign and magnitude of the correlation coefficient \rho. For the difference y = x_1 - x_2, the variance is given by \sigma_y^2 = \sigma_{x_1}^2 + \sigma_{x_2}^2 - 2 \rho \sigma_{x_1} \sigma_{x_2}, where a positive \rho (indicating that x_1 and x_2 tend to vary together) reduces \sigma_y^2 by counteracting the independent variances, potentially leading to substantial uncertainty cancellation if \rho approaches 1. This effect is particularly relevant in measurements where variables are derived from the same instrument or process, such as subtracting background noise from a signal. In the case of products, such as y = x_1 x_2, correlations influence the relative variance through the term $2 \rho \frac{\sigma_{x_1}}{\mu_{x_1}} \frac{\sigma_{x_2}}{\mu_{x_2}} in the approximation \left( \frac{\sigma_y}{y} \right)^2 \approx \left( \frac{\sigma_{x_1}}{x_1} \right)^2 + \left( \frac{\sigma_{x_2}}{x_2} \right)^2 + 2 \rho \frac{\sigma_{x_1}}{x_1} \frac{\sigma_{x_2}}{x_2}, where positive \rho increases the relative uncertainty while negative \rho decreases it, highlighting how dependencies can exacerbate or mitigate errors in multiplicative combinations like area calculations from length and width measurements. For instance, if two positively correlated lengths are multiplied, the propagated relative uncertainty may exceed the sum of individual relative uncertainties, emphasizing the need to account for \rho to avoid underestimating errors. Correlations enable uncertainty reduction in scenarios involving cancellation, such as in differential quantities where inputs share common error sources; for example, the uncertainty in a small difference \Delta x = x_1 - x_2 (like a change in position) diminishes if x_1 and x_2 are highly positively correlated, as the errors tend to offset each other rather than add randomly. This cancellation is crucial in fields like and , where repeated measurements under similar conditions introduce positive correlations that lower the effective variance in derived differentials. To estimate these correlation effects, the covariance (and thus \rho) can be computed from experimental data using the sample covariance formula \text{cov}(x_1, x_2) = \frac{1}{n-1} \sum (x_{1i} - \bar{x_1})(x_{2i} - \bar{x_2}), providing an empirical basis for propagation, or assumed from theoretical models of the measurement process, such as shared instrumental noise. As outlined in the incorporation of covariances, this structural inclusion allows for precise variance adjustments in practice.

Special Cases and Caveats

Reciprocals and Ratios

The propagation of uncertainty for reciprocal functions presents specific challenges due to the nonlinearity and potential for large relative uncertainties when the input is near zero. For the function y = 1/x, the standard approximation derived from the first-order Taylor series expansion yields the standard uncertainty \sigma_y \approx \frac{\sigma_x}{\mu_x^2}, where \mu_x and \sigma_x are the mean and standard uncertainty of x, respectively. This result follows directly from the law of propagation of uncertainty, with the sensitivity coefficient \left| \frac{\partial y}{\partial x} \right| = \frac{1}{\mu_x^2}. To mitigate issues arising from values of x near zero, where the function exhibits a singularity and the approximation may amplify uncertainties excessively, a shifted reciprocal form y = 1/(x + a) is often employed, with a chosen such that \mu_x + a is sufficiently distant from zero (e.g., a > \sigma_x). The corresponding uncertainty is then \sigma_y \approx \frac{\sigma_x}{(\mu_x + a)^2}, adapting the sensitivity coefficient to the shifted denominator. This adjustment maintains the validity of the linear propagation while avoiding numerical instability in calculations. Ratios, as in y = x_1 / x_2, extend these considerations to multivariate cases and are prevalent in measurements like or computations. The full variance formula, accounting for means, variances, and , is \sigma_y^2 = \frac{\mu_{x_1}^2 \sigma_{x_2}^2 + \mu_{x_2}^2 \sigma_{x_1}^2 - 2 \mu_{x_1} \mu_{x_2} \operatorname{Cov}(x_1, x_2)}{\mu_{x_2}^4}. This derives from applying the general propagation law to both partial derivatives: \frac{\partial y}{\partial x_1} = \frac{1}{\mu_{x_2}} and \frac{\partial y}{\partial x_2} = -\frac{\mu_{x_1}}{\mu_{x_2}^2}. If x_1 and x_2 are uncorrelated (\operatorname{Cov}(x_1, x_2) = 0), the formula simplifies to \sigma_y^2 = \frac{\mu_{x_1}^2 \sigma_{x_2}^2 + \mu_{x_2}^2 \sigma_{x_1}^2}{\mu_{x_2}^4}, highlighting the relative contributions from each input. A key caveat in both reciprocals and ratios is the potential for in the uncertainty estimates when the of the denominator approaches zero, as the relative \sigma_y / |\mu_y| can become excessively large even for modest \sigma_x / |\mu_x|. In such scenarios, the may underestimate the true spread, particularly if the inputs exhibit or heavy tails, necessitating higher-order methods or numerical approaches for accuracy.

Functions with Large Uncertainties

When the relative uncertainties in input variables are not small, the first-order Taylor expansion underlying standard linear propagation methods breaks down, as higher-order terms (quadratic and beyond) contribute substantially to the overall . This dominance introduces in the of the output quantity and often results in non-Gaussian distributions, rendering the assumption of invalid and potentially underestimating or overestimating the true variability. For instance, the variance of the output may no longer suffice as a complete descriptor, necessitating consideration of higher moments like to capture the full distributional behavior. In scenarios with large input uncertainties, probabilistic methods based on linear approximations can be supplemented or replaced by conservative techniques such as or worst-case analysis. propagates sets of possible input values through the function to yield enclosing intervals for the output, providing guaranteed bounds that encompass all feasible results without relying on distributional assumptions; this approach is particularly useful for non-linear functions where probabilistic estimates might miss extreme outcomes. Worst-case analysis similarly evaluates the function at boundary points of input intervals to establish possible outputs, offering a robust, albeit pessimistic, assessment that prioritizes safety over precision in and applications. Propagation through functions with large uncertainties frequently produces asymmetric error distributions, where the uncertainty intervals are not symmetric around the estimated value due to the non-linearity amplifying deviations differently in positive and negative directions. A classic example occurs in the of variables, yielding a for the output, which exhibits and a longer tail on one side, complicating the interpretation of standard deviations as symmetric errors. Such asymmetry highlights the need to report not just as a single standard deviation but potentially as separate upper and lower bounds to reflect the true risk profile. Practical diagnostics for assessing the validity of linear approximations include comparing the magnitudes of higher-order coefficients to the partial or evaluating the relative contribution of second-order terms in the , signaling the need for alternatives when these contributions are significant. When the function's (measured by second ) is pronounced within the region, switching to higher-order expansions or non-probabilistic bounds is recommended to maintain accuracy; tools like standardized indices can further quantify non-linearity impacts. Similar challenges, such as singularities near reciprocals, can amplify these effects in one direction, underscoring the importance of function-specific checks.

Numerical Methods

Monte Carlo Simulation

The provides a numerical approach to uncertainty propagation by approximating the of an output quantity through repeated random sampling of input variables according to their known or assumed distributions. This technique is particularly valuable when analytical methods, such as expansions, fail for complex, nonlinear models or when input uncertainties are large. In this approach, the functional relationship between inputs and output is evaluated many times using randomly generated input values, yielding a set of output values that empirically represent the output distribution. From this empirical distribution, key statistical measures—such as the , standard deviation, and coverage intervals—can be directly estimated without requiring closed-form expressions. The procedure begins with specifying the joint probability density function (PDF) for the input quantities, which may include marginal distributions (e.g., , , or triangular) and any correlations via copulas or matrices. A large number M of independent random vectors \mathbf{x}_i (for i = 1 to M) are then sampled from this joint PDF using pseudorandom number generators, such as the algorithm for high-quality deviates that can be transformed to other distributions. For each sampled vector \mathbf{x}_i, the model y = f(\mathbf{x}) is evaluated to produce a corresponding output value y_i. The collection of \{ y_i \} forms an empirical approximation of the output PDF, from which the () is computed as \hat{\mu}_y = \frac{1}{M} \sum_{i=1}^M y_i, the variance as \hat{\sigma}_y^2 = \frac{1}{M-1} \sum_{i=1}^M (y_i - \hat{\mu}_y)^2, and higher moments or quantiles as needed. This sampling enables the method to naturally incorporate dependencies between inputs by drawing from the joint distribution. Key advantages of the include its ability to handle highly nonlinear functions, correlated inputs, and non-Gaussian distributions without relying on approximations like or assuming of the output. Unlike analytical propagation formulas, it requires no of partial or sensitivity coefficients, making it straightforward to implement for arbitrary models, including those involving numerical simulations or iterative computations. The method also provides a full of the output , such as asymmetric distributions or PDFs, which is essential for in measurements where small input uncertainties can lead to significant output . These features have made it a recommended supplement to traditional methods in standards. Implementation involves selecting an appropriate sample size M to ensure of the estimates, typically ranging from $10^4 to $10^6 trials, where larger M reduces statistical variability but increases computational cost; can be assessed by monitoring the stability of quantiles or using criteria like the relative standard deviation falling below a (e.g., 0.1% for high ). must be reproducible and uniform to avoid biases, often achieved through standardized libraries in software like Python's or MATLAB's Statistics Toolbox. For multivariate cases with correlations, techniques such as of the facilitate efficient sampling from joint normals, while more general dependencies use methods like Nataf transformation. Variance estimation from the Monte Carlo samples follows standard statistical practice: the sample variance \hat{\sigma}_y^2 provides an unbiased estimate of the output variance, while the standard error of the mean is given by \text{SE}(\hat{\mu}_y) = \hat{\sigma}_y / \sqrt{M}, quantifying the precision of the mean estimate. Coverage intervals, such as the shortest 95% interval containing 95% of the y_i values, are obtained directly from order statistics of the sorted outputs, offering a distribution-free alternative to parametric confidence intervals and accounting for any asymmetry in the propagated uncertainty. These estimates improve with increasing M, and the method's reliability has been validated against analytical benchmarks in numerous metrological applications.

Bootstrap and Other Sampling Techniques

The bootstrap method provides a non-parametric approach to uncertainty propagation when empirical data is available, allowing estimation of the variability in a function's output without assuming a specific distributional form for the inputs. Introduced by Bradley Efron, the technique involves resampling the observed dataset with replacement to create multiple bootstrap samples, each of which is used to compute the function of interest, thereby generating an empirical distribution of the propagated uncertainties. This resampling process mimics the sampling variability inherent in the original data, enabling the approximation of the for complex functions where analytical methods fail. For deriving confidence intervals on propagated uncertainties, the percentile bootstrap is commonly employed, where the desired interval is obtained directly from the percentiles of the bootstrap distribution of the function values. This method is particularly useful for asymmetric distributions arising from non-linear propagations, as it avoids assumptions and provides robust bounds based solely on the empirical quantiles. Efron and Tibshirani highlight its simplicity and effectiveness in handling the often encountered in uncertainty estimates from real-world measurements. Other sampling techniques extend these ideas for improved efficiency or flexibility in non-parametric settings. enhances coverage of the input space by stratifying each into equal-probability intervals and pairing samples across dimensions, reducing the number of evaluations needed compared to simple random resampling while maintaining representativeness for uncertainty propagation in high-dimensional problems. Similarly, facilitates non-parametric reconstruction of input distributions from data, allowing propagation through or without parametric constraints, which is valuable when empirical densities are irregular or . In contrast to Monte Carlo simulation, which relies on specified parametric distributions for inputs, bootstrap and related resampling methods derive distributions directly from observed data, eliminating the need for distributional assumptions and making them suitable for empirical uncertainty quantification in data-rich scenarios.

Applications and Examples

Basic Formula Derivations

The propagation of uncertainty for the sum of two random variables begins with the general formula for the variance of their sum. For random variables X and Y, the variance is given by \operatorname{Var}(X + Y) = \operatorname{Var}(X) + \operatorname{Var}(Y) + 2 \operatorname{Cov}(X, Y). When X and Y are independent, the covariance term \operatorname{Cov}(X, Y) is zero, simplifying the expression to \operatorname{Var}(X + Y) = \operatorname{Var}(X) + \operatorname{Var}(Y). This result follows directly from the definition of independence, where the joint probability distribution factors into the product of the marginals, leading to the absence of correlation in their deviations. Consequently, the standard deviation of the sum, \sigma_{X+Y}, is \sqrt{\sigma_X^2 + \sigma_Y^2}, assuming uncorrelated uncertainties. For the product of two variables, consider y = x_1 x_2. Using differentials for small uncertainties, the relative change is \frac{\Delta y}{y} \approx \frac{\Delta x_1}{x_1} + \frac{\Delta x_2}{x_2}. This approximation arises from taking the total differential dy = x_2 \, dx_1 + x_1 \, dx_2, then dividing by y to obtain the relative form. For standard deviations under the assumption of small, uncorrelated errors, the relative uncertainty propagates in quadrature: \left( \frac{\sigma_y}{y} \right)^2 \approx \left( \frac{\sigma_{x_1}}{x_1} \right)^2 + \left( \frac{\sigma_{x_2}}{x_2} \right)^2, yielding \sigma_y \approx y \sqrt{ \left( \frac{\sigma_{x_1}}{x_1} \right)^2 + \left( \frac{\sigma_{x_2}}{x_2} \right)^2 }. The general approach to uncertainty propagation for a function y = f(\mathbf{x}) relies on a first-order Taylor series expansion around the mean values of the inputs. Expanding f(x + \Delta x) \approx f(x) + f'(x) \Delta x for a univariate case, the uncertainty in y is \Delta y \approx |f'(x)| \Delta x, or for standard deviations, \sigma_y \approx |f'(x)| \sigma_x. For the example y = x^2 + 2x, the derivative is f'(x) = 2x + 2, so \sigma_y \approx |2x + 2| \sigma_x. To derive this step-by-step: start with the Taylor expansion y(x + \delta x) = y(x) + y'(x) \delta x + \frac{1}{2} y''(x) (\delta x)^2 + \cdots; for small \delta x, neglect higher-order terms to get the linear approximation; the variance of y then approximates as \sigma_y^2 \approx [y'(x)]^2 \sigma_x^2, taking the absolute value for the standard deviation to ensure positivity. In the multivariate case, this extends to \sigma_y^2 \approx \sum_i \left( \frac{\partial f}{\partial x_i} \right)^2 \sigma_{x_i}^2 + 2 \sum_{i<j} \frac{\partial f}{\partial x_i} \frac{\partial f}{\partial x_j} \operatorname{Cov}(x_i, x_j). These derivations can be verified using simulated data by generating samples from distributions with known variances and computing the empirical variance of the resulting function values. For instance, draw N samples of x from a with mean \mu_x and standard deviation \sigma_x, apply the function to obtain y values, then compare the sample standard deviation of y to the predicted \sigma_y; as N increases, the simulated value converges to the analytical prediction under the assumptions of and small uncertainties.

Real-World Measurement Scenarios

In , the propagation of uncertainty is crucial when measuring the equivalent of resistors, where the total conductance G = 1/R is given by G = 1/R_1 + 1/R_2. To determine the uncertainty in the equivalent R, the uncertainties in the individual resistances \sigma_{R_1} and \sigma_{R_2} are propagated using the law of propagation of uncertainty from partial derivatives, assuming independence between R_1 and R_2. For example, consider two resistors measured as R_1 = 100 \, \Omega with \sigma_{R_1} = 1 \, \Omega and R_2 = 200 \, \Omega with \sigma_{R_2} = 2 \, \Omega. The equivalent is R = \frac{R_1 R_2}{R_1 + R_2} \approx 66.67 \, \Omega. The partial derivatives are \frac{\partial G}{\partial R_1} = -\frac{1}{R_1^2} and \frac{\partial G}{\partial R_2} = -\frac{1}{R_2^2}, leading to the variance in conductance \sigma_G^2 = \left( \frac{1}{R_1^2} \sigma_{R_1} \right)^2 + \left( \frac{1}{R_2^2} \sigma_{R_2} \right)^2 \approx 1.25 \times 10^{-8} \, \mathrm{S}^2, so \sigma_G \approx 1.12 \times 10^{-4} \, \mathrm{S}. Since \sigma_R = R^2 \sigma_G \approx 0.50 \, \Omega, the result is reported as R = 66.67 \pm 0.50 \, \Omega. This step-by-step approach demonstrates how small relative uncertainties in individual components amplify in the reciprocal relationship, as verified in laboratory demonstrations using printed circuit boards. In physics experiments involving coordinate measurements, such as determining the angle \theta = \tan^{-1}(y/x) from position data, uncertainty propagation accounts for errors in both x and y. The standard uncertainty in the angle is approximated by \sigma_\theta \approx \frac{ \sqrt{ y^2 \sigma_x^2 + x^2 \sigma_y^2 } }{ x^2 + y^2 }, derived from the general formula for functions of multiple variables under the assumption of uncorrelated inputs. For instance, in a optics setup measuring beam deflection where x = 3.00 \, \mathrm{m} with \sigma_x = 0.01 \, \mathrm{m} and y = 4.00 \, \mathrm{m} with \sigma_y = 0.01 \, \mathrm{m}, the angle is \theta \approx 53.13^\circ. Substituting into the formula yields \sigma_\theta \approx 0.11^\circ (converted from radians), providing a quantitative assessment of angular precision limited by positional errors. This method is particularly relevant in scenarios like contact angle measurements, where the tangent construction (inverse of arctan) introduces similar propagation effects near 90°, increasing uncertainty. Geometric measurements, such as the area A = l \times w of a , often involve correlated uncertainties when l and width w are measured with the same , introducing . The variance in area is \sigma_A^2 = w^2 \sigma_l^2 + l^2 \sigma_w^2 + 2 l w \mathrm{cov}(l, w), where \mathrm{cov}(l, w) captures the shared error source. Consider a rectangular plate measured with a caliper calibrated to \sigma = 0.05 \, \mathrm{cm}, yielding l = 10.0 \, \mathrm{cm} and w = 5.0 \, \mathrm{cm}; if the errors are fully correlated (e.g., \mathrm{cov}(l, w) = \sigma^2), then A = 50.0 \, \mathrm{cm}^2 with \sigma_A \approx 0.75 \, \mathrm{cm}^2. Without correlation, \sigma_A would be smaller at \approx 0.56 \, \mathrm{cm}^2, highlighting how ignoring underestimates in tool-dependent measurements. This term ensures realistic error estimates in engineering applications like dimensional . The interpretation and reporting of propagated uncertainties in these scenarios typically use the \pm notation for standard uncertainties or expanded intervals for specified coverage probabilities, such as 95% . According to guidelines, a result is expressed as y = y_0 \pm u(y) where u(y) is the standard , or with an expanded U = k u(y) (e.g., k=2 for approximately 95% coverage assuming ). For the parallel example, R = 66.67 \Omega \pm 0.50 \Omega (standard) or R = 66.67 \Omega \pm 1.00 \Omega (95% interval with k=2); similarly, \theta = 53.13^\circ \pm 0.11^\circ conveys the directly. This format facilitates comparison across experiments and emphasizes the reliability of the result within the stated bounds.