arXiv (All)
open access
A Bayesian approach to out-of-sample network reconstruction
Networks underpin systems that range from finance to biology, yet their structure is often only partially observed. Current reconstruction methods typically fit the parameters of a model anew to each snapshot, thus offering no guidance to predict future configurations. Here, we develop a Bayesian ap
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
- Bayesian Sampling of Structural Ensembles: The Role of Ensemble-Counting Measures#1296445
- Transferable reconstruction of nonlinear network dynamics from sentinel nodes#1064512
- How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?#1298260
- How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?#1291815
- The Statistical Cost of Causal Discovery with Feedback#1119123
Record · ID 1233786
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