UCSC-SOE-20-05: Privacy Preserving Efficient Computation in Bayesian High Dimensional Regression With Big Data

Rajarshi Guhaniyogi
06/22/2020 07:33 AM
Bayesian computation of high dimensional linear regression models with a popular Gaussian scale mixture prior distribution using Markov Chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive due to the heavy computational cost that grows in the cubic order of the number of predictors. We have proposed an approach that simultaneously preserves privacy of the data samples and makes computation efficient.