Sequential Gibbs Posteriors with Applications to Principal Component Analysis

no code implementations • 19 Oct 2023 • Steven Winter, Omar Melikechi, David B. Dunson

Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to the prior and providing a control of posterior uncertainty.

Bayesian Inference Uncertainty Quantification

Machine Learning and the Future of Bayesian Computation

no code implementations • 21 Apr 2023 • Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson

Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information.

Bayesian Inference Variational Inference

Interpretable AI for relating brain structural and functional connectomes

1 code implementation • 10 Oct 2022 • Haoming Yang, Steven Winter, Zhengwu Zhang, David Dunson

One of the central problems in neuroscience is understanding how brain structure relates to function.