A Statistical Inference Comparison for Measurement Estimation Using Stochastic Simulation Techniques

Abstract

The purpose of this paper is to present a comparison of different techniques for making statistical inference about a measurement system model. This comparison involves results when two main assumptions are made: 1) the unknowable behavior of the probability density function (pdf) p(e) of errors since the real measurement systems are always exposed to continuous perturbations of an unknown nature and 2) the assumption that, after some experimentation, one can obtain sufficient information that can be incorporated into the modeling as prior information.