CORDIS Project
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This project develops scalable Bayesian methods to analyze large datasets, addressing the limitations of existing algorithms. By combining advanced techniques from Bayesian nonparametrics and machine learning, it aims to improve the accuracy and acceptance of these methods in fields like medicine and cosmology.
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 limite…
UNIVERSITA COMMERCIALE LUIGI BOCCONI
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