Bayesian uncertainty analysis represents a powerful statistical framework that integrates prior knowledge with observed measurement data to quantify uncertainty in a consistent probabilistic manner.

Given the potentially strong, adverse effects of measurement error and the possibility of minimizing these using Hidden Markov models (HMMs), the aim of this thesis ...

In this study, we investigated bias caused by spatial variability and spatial heterogeneity in outdoor air-pollutant concentrations, instrument imprecision, and choice of daily pollutant metric on ...

The source of measurement error in population tables are not necessarily the result of sampling errors. In this sense, the proposed method is an extension of the variance of the regression estimator ...

All measurements are subject to error. When a reportable value is derived from a measurement or series of measurements, this value is only an estimate of the “true ...

This paper presents an EM algorithm for semiparametric likelihood analysis of linear, generalized linear, and nonlinear regression models with measurement errors in explanatory variables. A structural ...

Assess a discrete measurement. Perform analyzes for potential and long term control and capability. Make decisions on measurement systems process improvement. In this module, we will learn to identify ...

Ensuring parts meet specification is crucial and so it helps to have confidence in your measurement system. Parts that don’t meet specification, whether gauging composition or measuring thickness, ...