Exact and approximate Bayesian estimation of net counting rates.

Abstract

The stochastic fluctuations in the number of disintegrations, which had already been studied experimentally by Rutherford and other investigators at the beginning of the twentieth century, make estimation of net counting rates in the presence of background counts a challenging statistical problem. Exact and approximate Bayesian estimates of net count rates using Poisson and normal distributions for the number of counts detected during varying counting intervals are derived. The posterior densities for the net count rate are derived and plotted for uniform priors. The graphs for the exact, Poisson based, and for the approximate posterior densities of the background and net count rates, resulting from the normal approximation to the Poisson distribution, were compared. No practical differences were found when the number of observed gross counts is large. Small numerical differences in the posterior expectations and standard deviations of the counting rates appeared when the number of observed counts was small. A table showing some of these numerical differences for different background and gross counts is included. A normal approximation to the Poisson is satisfactory for the analysis of counting data when the number of observed counts is large. Some caution has to be exercised when the number of observed counts is small.