报告题目：Multi-model estimation and information theory for improving probabilistic predictions of complex systems

报告人：Michal Branicki 博士

报告时间：2019年4月2日（星期二）下午13:30-14:30

报告地点：创新园大厦A1101

报告校内联系人：牛一 副教授             联系电话：84708351-8025

报告摘要：Multi Model Ensemble (MME) predictions are a popular ad-hoc technique for improving state estimates of high-dimensional, multi-scale dynamical systems, including Global Circulation Models (GCM’s) used in climate change predictions. The heuristic idea behind the MME framework is simple: given a collection of models, one considers predictions obtained through a convex superposition of the individual forecasts in the hope of mitigating modelling errors. However, it is not obvious if this is a viable strategy and which models should be included in the MME forecast in order to achieve the best predictive performance. I will briefly describe how an information-theoretic approach to this problem allows for deriving systematic criteria for improving dynamical predictions within the MME framework, and how such a framework can aid data assimilation techniques which are based on multi model ensembles.

报告人简介：Dr Michal Branicki works on the interface of probability theory and stochastic dynamical systems with applications to quantifying uncertainty in prediction problems arising in data science. He is particularly interested in mathematical aspects of information theory, Bayesian data assimilation and machine learning, and techniques for systematic simplification of complex systems using empirical data. Michal is also a Faculty Fellow at the Alan Turing Institute which is a national centre for Data Science in the UK. In his teaching Michal specialises in Dynamical Systems and Probability theory. 

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                                                     2019年4月1日