title: Simultaneous Source Detection and Analysis Using a Zero-inflated Count Rate Model.
abstract: This paper proposes a novel Bayesian technique that allows for simultaneous source detection and count rate analysis. The technique involves using priors, which include a finite probability that the source count rate is exactly zero. Such priors are called "zero-inflated." Solving the posterior distribution of a zero-inflated count rate model provides the probability that the sample contains a source and a probability distribution for the source count rate if the source exists, without the need to perform redundant computations. Sampling from zero-inflated distributions is straightforward and can be accomplished with easily accessible open source software. In addition, zero-inflated priors lead to finite posterior probabilities of "no source," which is an easy-to-understand and satisfying result.
authors.affiliation: Colorado State University, 399 MRB. 1681 Campus Delivery, Fort Collins, CO 80523-1681.
authors.forename: John
authors.initials: J
authors.lastname: Klumpp
authors.forename: Alexander
authors.initials: A
authors.lastname: Brandl
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D012907:Smoking