🤖 AI Summary
This study addresses the lack of theoretical guarantees regarding the impact of predictive design distributions on inference validity in high-dimensional Bayesian regression. The authors propose a novel parametric martingale posterior approach based on sequential one-step-ahead predictive quantities, which operates without Markov chain Monte Carlo methods and, for the first time, systematically elucidates the critical role of predictive design distributions. The method satisfies weak identifiability and design invariance, and integrates predictive resampling with high-dimensional regularization techniques. In simulation experiments, it demonstrates both computational efficiency and stable performance in Bayesian predictive inference.
📝 Abstract
The predictive approach to Bayesian inference accesses the posterior distribution via a sequence of one-step-ahead predictives, enabling inference via predictive resampling without Markov chain Monte Carlo. In the random-design regression setting, an explicit specification of the predictive design distribution is required, yet the impact of this choice has received little formal attention. We study the role of this predictive design distribution in parametric martingale posteriors for regression, and identify predictive notions of identifiability and design invariance that are essential for valid inference, particularly in the high-dimensional regression setting. Building on these foundations, we introduce a novel class of parametric martingale posteriors for regression that satisfies a weak form of these desiderata, and naturally accommodates the high-dimensional setting through regularization. We then illustrate our method through a simulation.