Application of Bayesian Neural Networks in High Energy Physics Experiments

Some applications of Bayesian neural networks (BNN) in the high energy physics experiments are described in the present paper. They are the applications of BNN to particle identification in the second generation of Beijing spectrometer experiment (BESII), event identification and event reconstruction in reactor neutrino experiments and supernova location in scintillator detector experiments, respectively. Compared to traditional method, better results are obtained in those experiments using BNN. So we believe that BNN can be also well applied to other fields in other experiments for the high energy physics.