Bayesian Model Averaging under Predictor Redundancy via Density-Ratio Posterior Compression
Bayesian model averaging in support-indexed regression induces a posterior distribution over active predictor supports. Under predictor redundancy, posterior mass can spread across many nearly interchangeable supports, making exact-support summaries unstable or hard to interpret even when prediction is stable. We study how to report an already fitted Bayesian model averaging posterior without changing the Bayesian target. A report uses hard or soft regions of support space, and its compressed reporting law is compared with the reference posterior through an explicit density ratio. This ratio gives computable total-variation and Kullback--Leibler distortion, bounds for bounded predictive s...
| Authors | Hanqing Li, Xuewen Lu, Yuting Chen |
|---|---|
| Primary category | stat.ML |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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