CORDIS Project
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This project aims to develop Bayesian nonparametric methods for modeling complex data structures, including networks and consumer preferences. It focuses on creating models that adapt to large datasets, enhancing predictive capabilities in e-commerce.
Bayesian nonparametric (BNP) methods have become very popular over recent years in machine learning and statistics as it allows to build elegant and sophisticated models.
Contrary to Bayesian parametric methods, this set of techniques allows the number of parameters to grow with the number of data and is particularly suitable in the data rich environment we now face.
This project aims at developing new Bayesian models for the probabilistic modeling of large and structured data such as networks a…
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
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