A multi-chain sampling method based on Metropolis-Hastings algorithm was used to improve the Markov Chain Monte Carlo(MCMC) method in order to prevent from trapped into the local optimal solutions that often occur to probability inversion by using current MCMC algorithm.
概率反演中,马尔科夫链蒙特卡罗是一类重要的后验概率抽样方法,但由于该算法的搜索往往会陷入局部最优解,因而限制了其在具有非唯一解反问题中的应用。
