Climate Change Data: Use of an Autoregressive (AR) Model in Presence of Change Points under a Bayesian Approach

In this study, we introduce a statistical model applied to climate change data consisting of an autoregressive times series (AR) which represents type random process. A Bayesian approach using MCMC (Markov Chain Monte Carlo) methods is considered get the inferences interest. The main goal study have good predictions for mean temperature and also identify time possible change-points that might be present in could indicate beginning climate. Applications proposed are annual average temperatures some locations obtained over period ranging from end 1800’s popular discrimination criterion methods.In addition fit data, was used detect changes different stations CUSUM methodology.