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 ...

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

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 ...