Bayesian Discovery Sampling: A Simple Model of Bayesian Inference in Auditing

摘要

Once an auditor has been convinced of the advantages of Bayesian inference, hehas other problems to solve: How can he objectively specify his prior knowledge about the population; How can he objectively interpret posterior probabilities to decide how to audit this population. The paper answers the questions by showing that the methodology of discovery sampling gives all the information needed to specify the prior and to interpret the posterior densities. The results in a Bayesian version of a methodology that has already been used by auditors for a number of years. By using the Bayesian model of discovery sampling presented in the paper, auditors are not only able to reduce sample sizes but will also exactly know the importance of the assumptions they have made in order to achieve this efficiency.