主讲人: 孟晓犁教授, 哈佛大学统计系主任（COPSS 奖获得者）
时 间: 2007.7.12, 4:00-6:00 p.m.
地 点: C405
摘 要: In business, finance, and other endeavors that require constant decision making, effectively combining quantitative information with subjective judgment is critical in achieving success and maintaining competitiveness. Bayesian methodology is ideally suited for this task because it provides a coherent and rigorous probability-based framework for statistical analysis with explicit subjective input via prior specification. The first part of this tutorial demonstrates the modeling aspects of Bayesian Statistics via a couple of examples on airline stocks and insurance mortality. Aided by movies, the second part introduces Markov chain Monte Carlo (MCMC), a general class of simulation methods that have revolutionized the Bayesian Statistics since 1990s because they made it possible to fit many realistic Bayesian models that defeat traditional computational methods.