Nonlinear correlated output propagation
Research context
postgraduate concept · established · As of 2026-10-10
For a vector measurand Y=f(X), propagating a joint input distribution retains output dependence and non-Gaussian shape.
Key knowledge
For a vector measurand Y=f(X), propagating a joint input distribution retains output dependence and non-Gaussian shape. Local U_Y≈J U_X Jᵀ is only a linearization. For the explicit X∼N(0,u²), Y=(X,X²), Cov(Y₁,Y₂)=0 although the outputs are dependent; a covariance matrix alone need not specify their joint distribution.
Values and equations
E[X²]=u²; Var(X²)=2u⁴; Cov(X,X²)=0
- X,u
- Gaussian input and standard uncertainty Q
- X²
- Squared output Q²
- Var(X²)
- Variance of squared output Q⁴
- Cov(X,X²)
- Cross covariance Q³
Conditions and scope
- Declared joint input model and finite moments; the example uses one scalar input with units Q.
- The nonlinear example is an analytic moment calculation, not a new experimental result. Approximate linearization fails for the square component at zero because its derivative vanishes.
Sources · 1
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Inspected §§5–8: joint input distribution, nonlinear propagation, Gaussian regions and adaptive Monte Carlo validation; source check dated 2026-10-10. Scope is limited to the cited statements, equations and assumptions, not the whole publication.
2026-10-10 · Author source inspection of the displayed claim, model assumptions and equations; independent exact-content and branch-inventory review is a separate gate. Contemporary status is a dated snapshot, not a promise of ongoing monitoring.
Entry details
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- research-measurement-nonlinear-vector-propagation
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- research.1
- Structured reference record
- Postgraduate & research reference