论文标题
贝叶斯计算的近似方法
Approximate Methods for Bayesian Computation
论文作者
论文摘要
在这个信息时代，丰富的数据生成机制无处不在，需要复杂的统计模型来提出有意义的推断。尽管贝叶斯分析在过去的30年中取得了巨大的发展，但从成功应用马尔可夫链蒙特卡洛（MCMC）采样的动力中受益，大数据和复杂模型的结合共同为传统的MCMC算法带来了重大挑战。我们回顾了现代算法发展，以解决后者，并使用数值实验比较其性能。
Rich data generating mechanisms are ubiquitous in this age of information and require complex statistical models to draw meaningful inference. While Bayesian analysis has seen enormous development in the last 30 years, benefitting from the impetus given by the successful application of Markov chain Monte Carlo (MCMC) sampling, the combination of big data and complex models conspire to produce significant challenges for the traditional MCMC algorithms. We review modern algorithmic developments addressing the latter and compare their performance using numerical experiments.
