arXiv (All)
open access
Copula Active Subspaces I: A Score-Covariance Method for Reduced-Order Non-Gaussian Density Estimation
In Bayesian inference problems with non-Gaussian observation noise, the posterior is only as accurate as the noise density, and gradient-based samplers need that density and its gradient evaluable pointwise, whether from an explicit expression or from code, and without an inner solve. We propose Cop
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
- Compressed Active Subspaces for Scalable Bayesian Inference#1014132
- Constrained Denoising, Empirical Bayes, and Optimal Transport#996866
- Bayesian Modular Inference for Copula Models with Potentially Misspecified Marginals#1052105
- Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation#684140
- Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation#682365
Record · ID 1130984
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