Efficient posterior sampling for high-dimensional imbalanced logistic regression

Understand

High-dimensional data are routinely collected in many areas.

• We are particularly interested in Bayesian classification models in which one or more variables are imbalanced.
• Current Markov chain Monte Carlo algorithms for posterior computation are inefficient as $n$ and/or $p$ increase due to worsening time per step and mixing rates.
• One strategy is to use a gradient-based sampler to improve mixing while using data sub-samples to reduce per-step computational complexity.