Abstract: In this talk, I will first provide an overview on parametric statistical change point analysis.Statistically speaking, a change point is defined as, in a broad sense, the unknown point k, such that observations before point k are different from observations after point k. Multiple change points can be similarly defined.   One of the key features of statistical change point analysis is to estimate the unknown change point location for various statistical models imposed on the sample data. This analysis can be done through a hypothesis testing process, a model selection perspective, a Bayesian approach, among other methods. Change point analysis has a wide range of applications in research fields such as statistical quality control, finance and economics, climate study, medicine, genetics, etc. I will present two change point models and their solutions, one is a likelihood procedure test statistics based approach and the other is a Bayesian solution. I will also provide an application on detecting changes in Dow Jones Weekly stock price index, as well as an application on identifying boundaries of DNA copy number variation (CNV) regions on breast cancer/tumor data.