直观理解 MCMC（第一部分）：梅特罗波利斯-哈斯廷斯算法

The Metropolis-Hastings algorithm is a crucial method within the Markov Chain Monte Carlo (MCMC) framework, widely utilized in high-end quantitative finance. This algorithm generates samples by constructing a Markov chain, aiding in the resolution of complex probability distribution problems. Its core principle revolves around an acceptance-rejection mechanism that incrementally approaches the target distribution for effective sampling. Key steps in the implementation include selecting an appropriate proposal distribution and determining the acceptance probability, both of which are vital for the accuracy of the final results. A deeper understanding of the Metropolis-Hastings algorithm enhances the grasp of its applications in financial modeling and risk management.