BAYESIAN-ANALYSIS OF ERRORS-IN-VARIABLES REGRESSION-MODELS

Use of errors-in-variables models is appropriate in many practical exp erimental problems. However, inference based on such models is by no m eans straightforward. In previous analyses, simplifying assumptions ha ve been made in order to ease this intractability, but assumptions of this nature are unfortunate and restrictive. In this paper, we analyse errors-in-variables models in full generality under a Bayesian formul ation. In order to compute the necessary posterior distributions, we u tilize various computational techniques. Two specific non-linear error s-in-variables regression examples are considered; the first is a re-a nalysed Berkson-type model, and the second is a classical errors-in-va riables model. Our analyses are compared and contrasted with those pre sented elsewhere in the literature.