ERC Starting Grant · 2022
Provable Scalability for high-dimensional Bayesian Learning
As the scale and complexity of available data increase, developing rigorous understanding of the computational properties of statistical procedures has become a key scientific priority of our century. In line with such priority, this project develops a mathematical theory of computational scalability for Bayesian learning methods, with a focus on extremely popular high-dimensional and hierarchical models. Unlike most recent literature, we will integrate computational and statistical aspects in the analysis of Bayesian learning algorithms, providing novel insight into the interaction between commonly used model structures and fitting algorithms. Key methodological breakthroughs will include…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.