arXiv (All)
open access
Bayesian Effective Dimension from Information Growth
Nominal dimension can greatly overstate how much a Bayesian model actually learns from data. We quantify this gap through a prior-dependent effective dimension based on parameter--data mutual information, normalized so that regular parametric information growth provides the benchmark scale. The main
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
- On efficiency gains via augmenting a tiny sample with a massive auxiliary sample#780754
- On efficiency gains via augmenting a tiny sample with a massive auxiliary sample#779379
- A note on the Simile of the Rout in the Posterior Analytics ii 19#94267
- W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators#972850
- Bayesian estimation in multiple comparisons#431682
Record · ID 820314
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