arXiv (All)
open access
Bagged Martingale Posteriors: Calibrated Uncertainty Quantification for Predictive Resampling
Martingale posteriors and related predictive resampling methods replace the likelihood--prior pair used within Bayesian inference with a predictive model for future observations. These methods are simple to implement and increasingly popular due to their computational efficiency, but little is known
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Model-assisted estimation with a training subsample: a two-phase sampling approach with design-based variance estimation#926925
- Efficient model exploration with the integrated nested Laplace approximation#975334
- Post-Corrected Raw-Score Martingale Posterior Sampling for von Mises-Fisher Models#972047
- Calibrated Predictive Distributions from Sample-Based Generators#1013246
- Estimating dynamic models by matching random features#925211
Record · ID 1105181
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive