Updating beliefs for binary variables

We consider the problem of updating beliefs for binary random variables, when probability assessments are elicited for them based on information of varying quality. We propose the threshold model, a Bayesian updating procedure where only measures of location and correlation have to be specified before any updating is possible. The main aspect of this model is the use of Jeffrey's conditionalization. According to this rule, it is not necessary to model the assessments and how they relate to the quantities of interest in a fully parametric way. This paper is motivated by the practical issue where a large company needs to manage its assets and future expenditure.