蒙特卡罗方法在核类本科专业的教学初探

蒙特卡罗方法是以概率统计理论为基础、通过计算机编程而实现的一种数值计算方法,在核科学与技术等领域有着广泛的应用。新时期下,初步尝试面向核类本科专业开设蒙特卡罗方法这门课程,将概率统计思想和编程实践训练融入到蒙特卡罗方法教学中,教学取得了一定成效。     

A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model

Computationally efficient methods for Bayesian analysis of seemingly unrelated regression (SUR) models are described and applied that involve the use of a direct Monte Carlo (DMC) approach to calculate Bayesian estimation and prediction results using diffuse or informative priors. This DMC approach is employed to compute Bayesian marginal posterior densities, moments, intervals and other quantities, using data simulated from known models and also using data from an empirical example involving firms’ sales. The results obtained by the DMC approach are compared to those yielded by the use of a Markov Chain Monte Carlo (MCMC) approach. It is concluded from these comparisons that the DMC approach is worthwhile and applicable to many SUR and other problems.