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The missing mathematical story of Bayesian uncertainty quantification for big data

CORDIS · observation · Publication date unknown

ssing mathematical story of Bayesian uncertainty quantification for big data Recent years have seen a rapid increase in available information. This has created an urgent need for fast statistical and machine learning methods that can scale up to big data sets. Standard approaches, including the now routinely used Bayesian methods, are becoming computationally infeasible, especially in complex models with many parameters and large data sizes. A variety of algorithms have been proposed to speed up these procedures, but these are typically black box methods with very limited theoretical support. In fact empirical evidence shows the potentially bad performance of such methods. This is especially concerning in real-world applications, e.g. in medicine. In this project I shall open up the black box and provide a theory for scalable Bayesian methods combining recent, state-of-the-art techniques from Bayesian nonparametrics, empirical process theory, and machine learning. I focus on two very important classes of scalable techniques: variational and distributed Bayes. I shall establish guaran

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recordType

award

status

SIGNED

region

EU

value

1492750

unit

EUR