Stochastic majorization--minimization algorithms for data science

采集和存储数据的性质不断变化，使得传统的统计和机器学习方法无法进行推断。现代数据通常是在真实的时间内以增量的方式获取的，并且通常可获得的数据量太大而无法在传统机器上处理。该项目提出了研究家庭的随机优化，最小化算法的增量方式计算的推理量。所提出的随机算法涵盖并扩展了用于拟合统计和机器学习模型的各种当前算法框架，并且可以用于为当前和未来的复杂模型产生可行且实用的算法。

The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inferential quantities in an incremental manner. The proposed stochastic algorithms encompass and extend upon a wide variety of current algorithmic frameworks for fitting statistical and machine learning models, and can be used to produce feasible and practical algorithms for complex models, both current and future.