Monte Carlo Simulation for Textile Uncertainty Propagation
topic
Monte Carlo uncertainty propagation in complex textile measurement models generates large numbers of random input quantity values from their assigned probability distributions and propagates them through the measurement function to build an empirical output distribution, supplementing analytical GUM propagation when non-linear measurement models or non-Gaussian input distributions make analytical approximations invalid.
Role
Provides a computationally accessible alternative to analytical uncertainty propagation for complex textile test calculations including colour difference formulae, multi-parameter comfort indices, and composite durability metrics where non-linearity and asymmetric input distributions make GUM analytical approximations inadequate.
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