eScholarship
metadata only
Bayesian Inference over Data Distributions: A Gaussian Process Approach
We present BI*, a Bayesian inference framework that places beliefs over data and data patterns rather than model parameters. Unlike conventional Bayesian approaches that require specifying parametric models, BI* operates directly on the space of possible data-generating distributions—one of which re
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 models and simulations in cognitive science#702151
- A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity#75774
- Bayesianizm w polskiej tradycji probabilizmu – studium stanowiska Kazimierza Ajdukiewicza#744140
- Structural Bayesian Inference: Theory-Space Learning in the Structural Descent Framework#108295
- Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So#198379
Record · ID 1079021
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