Abstract
AbstractWe introduce and develop a weighted Bayesian bootstrap (WBB) for machine learning and statistics. WBB provides uncertainty quantification by sampling from a high dimensional posterior distribution. WBB is computationally fast and scalable using only off‐the‐shelf optimization software. First‐order asymptotic analysis provides a theoretical justification under suitable regularity conditions on the statistical model. We illustrate the proposed methodology in regularized regression, trend filtering and deep learning and conclude with directions for future research.
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