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.
翻译: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才能得出有意义的推论。 过去30年来,贝叶斯人的分析取得了巨大发展,得益于成功应用Markov连锁链蒙特卡洛(MCMC)取样带来的动力,大数据和复杂模型相结合,给传统的MCMC算法带来了重大挑战。 我们审查了现代算法的发展,并用数字实验来比较其性能。