Yang, T.E. and Kuo, L. (2001) Bayesian Binary Segmentation Procedure for a Poisson Process with Multiple Change-Points. Journal of Computational and Graphical Statistics, 10, 772-785.
http://dx.doi.org/10.1198/106186001317243449

has been cited by the following article:

• TITLE: Analysis of Ozone Behaviour in the City of Puebla-Mexico Using Non-Homogeneous Poisson Models with Multiple Change-Points

  AUTHORS: Juan Antonio Cruz-Juárez, Hortensia Reyes-Cervantes, Eliane R. Rodrigues

  KEYWORDS: Non-Homogeneous Poisson Model, Markov Chain Monte Carlo Methods, Bayesian Inference, Ozone Air Pollution, City of Puebla

  JOURNAL NAME: Journal of Environmental Protection, Vol.7 No.12, November 25, 2016

  ABSTRACT: In this work, some non-homogeneous Poisson models are considered to study the behaviour of ozone in the city of Puebla, Mexico. Several functions are used as the rate function for the non-homogeneous Poisson process. In addition to their dependence on time, these rate functions also depend on some parameters that need to be estimated. In order to estimate them, a Bayesian approach will be taken. The expressions for the distributions of the parameters involved in the models are very complex. Therefore, Markov chain Monte Carlo algorithms are used to estimate them. The methodology is applied to the ozone data from the city of Puebla, Mexico.