A Bayesian Approach to Estimation of Metal Flows in Waste Streams

Abstract

Metal contents in waste are normally presented without any measures of uncertainty, and, if such measures are given, they are normally based on subjective judgements. Moreover, it is difficult to analyse existing data sets by employing traditional statistical procedures. The number of analysed samples can be very small and the sampling strategy is often unknown. Outliers are common and it is not unusual that the standard deviation is larger than the mean. In this paper, we propose a method that combines subjective judgement of uncertainty with actual observations to establish uncertainty bounds. The Bayesian statistical framework is used to set up a probability model that reflects our scientific problem, and this provides a theoretical basis for the inference problem.