A Bayesian approach for locating change points in a compound Poisson process with application to detecting DNA copy number variations

This work examines the problem of locating changes in the distribution of a Compound Poisson Process where the variables being summed are iid normal and the number of variable follows the Poisson distribution. A Bayesian approach is developed to identify the location of significant changes in any of the parameters of the distribution, and a sliding window algorithm is used to identify multiple change points. These results can be applied in any field of study where an interest in locating changes not only in the parameter of a normally distributed data set but also in the rate of their occurrence. It has direct application to the study of DNA copy number variations in cancer research, where it is known that the distances between the genes can affect their intensity level.