Monte Carlo uncertainty propagation
Sample the joint input model to approximate the output distribution.
Key knowledge
Monte Carlo propagation draws input vectors from their assigned joint distribution and evaluates the measurement model. The resulting output sample estimates its distribution, central value, uncertainty and coverage interval.
Values and equations
x^(r) ∼ joint input distribution; y^(r) = f(x^(r)), r = 1,…,M
- x^(r)
- Joint input draw with each component's physical unit
- y^(r)
- Corresponding output value; measurand unit
- M
- Number of simulation trials
- f
- Valid measurement model
Conditions and scope
- Preserve input dependencies, valid model domains and relevant finite moments; establish numerical convergence/tolerance.
- Significant nonlinearity can affect the central estimate as well as its uncertainty; simulation does not repair poor input knowledge.
Sources · 2
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Bureau International des Poids et Mesures (BIPM)2 sources
Evidence and review
JCGM 101 sections 5.9.6, 7.3–7.9; 2026 amendment to GUM 4.1.4 and H.1.7: nonlinear estimate correction.
2026-10-10 · Distribution propagation method only; no fixed trial-count guarantee or experimental validation claim.
Entry details
Permanent link to this entry ↗- Record identity
- measurement-monte-carlo-propagation
- Revision
- university-reference.1
- Structured reference record
- University reference · University core expansion