摘要
在贝叶斯模型中,往往无法解析计算后验概率,在实践中依赖于近似推理。变分推理(VI,Variational Inference)是重要的确定性近似推理方法,比马尔科夫链蒙特卡罗(MCMC,Markov Chain Monte Carlo)采样具有更高的计算效率,在大数据时代有着显著优势。文章通过贝叶斯分层模型,回顾了经典VI,分析了随机变分推理(SVI,Stochastic Variational Inference)及其在主题模型中的应用,综述了更稳健的VI,概述了概率编程系统的研究进展,并对VI的未来发展趋势做出了展望。
In Bayesian model,it is often impossible to calculate the posterior analytically,and in practice,it relies on approximate inference.Variational Inference(VI)is an important deterministic approximate inference method,which has a higher computational efficiency than Markov Chain Monte Carlo(MCMC)sampling,and has a distinct advantage in the era of big data.This paper reviewed classical VI,analyzed Stochastic Variational Inference(SVI)and its application in subject model,reviewed more robust VI,summarized the research progress of probabilistic programming system,and prospected the future development trend of VI.
作者
令晓明
范少良
王文强
顾䶮楠
张凯越
LING Xiaoming;FAN Shaoliang;WANG Wenqiang;GU Yannan;ZHANG Kaiyue(National Engineering Research Center for Technology of Environmental Deposition,Lanzhou Jiaotong University,Lanzhou 730070,China;Key Laboratory of Opto-technology and Intelligent Control of Ministry of Education,Lanzhou Jiaotong University,Lanzhou 730070,China;School of Automation and Electrical Engineering of Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《铁路计算机应用》
2022年第3期6-11,共6页
Railway Computer Application
基金
国家自然科学基金项目(16663021)。
关键词
贝叶斯
变分推理
鲁棒变分推理
主题模型
概率编程
Bayes
variational inference(VI)
topic model
robust VI
probabilistic programming
