Using Bayesian Approach of Mixture Poisson Regressions Model to Estimate the Propagation Rate of a Viral Infection Potato Plants - Kekoura, Sakouvogi; Liu, Xiyuan (Journal of Advance Research in Mathematics and Statistics (ISSN 2208-2409), 2017)
The mixture model is widely used in many clustering problems. Hence, the estimation approaches for the mixture model are also developed. The most well-known method is called EM algorithm, which is a method based on the frequentist statistics. However, the frequentist statistics approach is based on the fixed parameter estimation which is less accurate comparing with the alternative approach – Bayesian approach. The Bayesian approach estimates the parameter based on the data set, thus, the model via Bayesian approach is theoretically better fits with the corresponding to the data set. This paper will introduce the Mixture Poisson Regression Model with Bayesian approach. In addition, this paper compares this model with the mixture of linear regression which has been used in this data set.