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

Asynchronous and Distributed Data Augmentation for Massive Data Settings

1 code implementation • 18 Sep 2021 • Jiayuan Zhou, Kshitij Khare, Sanvesh Srivastava

The extended DA algorithm is indexed by a parameter $r \in (0, 1)$ and is called Asynchronous and Distributed (AD) DA with the original DA as its parent.

Bayesian Inference Data Augmentation +1

An Asynchronous Distributed Expectation Maximization Algorithm For Massive Data: The DEM Algorithm

4 code implementations • 20 Jun 2018 • Sanvesh Srivastava, Glen DePalma, Chuanhai Liu

The E step of DEM algorithm is performed in parallel on all the workers, and every worker communicates its results to the managers at the end of local E step.

Robust and Scalable Bayes via a Median of Subset Posterior Measures

no code implementations • 11 Mar 2014 • Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson

We propose a novel approach to Bayesian analysis that is provably robust to outliers in the data and often has computational advantages over standard methods.