报告题目：Bayesian Inference for Process Data Analytics(过程数据分析的贝叶斯推断)
报 告 人： Biao Huang (黄彪) （加拿大阿尔伯塔大学教授、加拿大工程院院士、IEEE Fellow）
报告时间：2018年10月26日下午14:00分（星期五）
报告地点：安徽大学磬苑校区理工A楼102报告厅
报告摘要：Bayesian theory, due to its mathematical rigor and application flexibility, has attracted great interests from both academia and practitioners. The original Bayesian rule, as a single formula, can evolve into pages of long mathematical derivations. Yet the end result provides very meaningful solutions to the practical problems. Although the control community may not be very familiar with the term “Bayesian”, it has been adopted by control scientists as early as the start of modern control. The most well known application of Bayesian theory in control engineering is Kalman filter which has been widely adopted by the control community. It is now commonly recognized that many control engineering related problems can be formulated under Bayesian framework and readily solved. Bayesian inference is getting even more popular due to the growing interest in Big Data and Data Analytics. This presentation will give a historical overview of Bayesian methods in control engineering, current activities, and future trends.
主办单位：电气工程与自动化学院
欢迎各位老师、同学届时前往！
科学技术处
2018年10月22日