Major accidents are low frequency high consequence events which are not well supported by conventional statistical methods due to data scarcity. In the absence or shortage of major accident direct data, the use of partially related data of near accidents – accident precursor data – has drawn much attention. In the present work, a methodology has been proposed based on hierarchical Bayesian analysis and accident precursor data to risk analysis of major accidents. While hierarchical Bayesian analysis facilitates incorporation of generic data into the analysis, the dependency and interaction between accident and near accident data can be encoded via a multinomial likelihood function. We applied the proposed methodology to risk analysis of offshore blowouts and demonstrated its outperformance compared to conventional approaches.