1 citations · 1 across the 3 of their papers we have counts for
3 papers
Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data
Ian Hultman, Kirtikanth Kalapatapu, Yassine Filali +2
Complex-valued arrays arise in signal processing, where scientific interpretation depends on retaining amplitude and phase information. Existing covariance estimation methods eithe…
Bayesian Compressed Mixed-Effects Models
Sreya Sarkar, Kshitij Khare, Sanvesh Srivastava
Penalized likelihood and quasi-likelihood methods dominate inference in high-dimensional linear mixed-effects models. Sampling-based Bayesian inference is less explored due to the…
Machine Learning and the Future of Bayesian Computation
Steven Winter, Trevor Campbell, Lizhen Lin +2
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they…