A Bayesian Inference Method under Data-Intensive Computing

Along with the development of information technology, data-intensive computing has become a research hotspot and it also proposed a new challenge to traditional Bayesian inference methods. It is known that, among different Bayesian inference methods, random algorithm often been regarded as a common and effective one. And the sampling method adopted in random algorithm would largely influence the efficiency of this random algorithm. Gibbs sampling method often been used in random algorithm for Bayesian inference. Taking all of this into consideration, a Bayesian inference method under data-intensive computing is developed in this paper, which first use improved Gibbs sampling method in each station to gain the suitable information, then union them together to infer the final result. The validity of this method is discussed in theory and illustrated by experiment.