A Finite Population Bayesian Model for Compliance Testing
提出有限贝叶斯程序（FBP）作为计算内部控制属性不合规率上限的替代方法，利用先验信息并假设有限总体，优于经典程序和无限贝叶斯程序。
In this paper we present an alternative procedure for establishing an upper precision limit (UPL) for the rate of noncompliance of an internal control attribute. This procedure is based on Ericson's [1969] pioneering work in which he applied Bayesian methods to finite population sampling. We refer to our proposed procedure as the finite Bayesian procedure (FBP). The FBP is presented as an alternative to both the classical procedure (CP) and the infinite Bayesian procedure (IBP). The classical procedure for computing a UPL uses any one of the three well-known sampling distributions for number of errors: the hypergeometric, binomial, or Poisson. The CP does not use prior information, and the only variability that is accounted for is that which is due to the sampling design. Felix and Grimlund [1977] introduced the IBP as the first alternative to the classical procedure. The IBP utilizes prior information through a prior distribution and makes a posterior probability statement about the process error rate. The IBP does not account for the fact that most audit populations of attributes are finite. This is also true of the CP when it uses the binomial or Poisson distribution. The FBP correctly assumes a finite population, but in addition it exploits the auditor's prior information through a prior distribution. In contrast to the IBP, the FBP makes a conclusion about the value of interest, the finite population error rate, rather than the process error rate. Our main conclusions are: (1) the finite Bayesian model emulates the