eScholarship
metadata only
Bayesian Regression for Dependent Tensor-Valued Data from Exponential Families
In the Earth system sciences, identifying physically meaningful patterns from dependent, high-dimensional, tensor-valued data is important for understanding variability in the Earth system. Orthonormal matrices are fundamental for statistical modeling of dependent data due to their role in matrix an
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
- Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings#924054
- Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings#919825
- A Christoffel-like function for high-dimensional support inference in graphical models#124644
- SLP-ProbHard: Probabilistic Hard-Constrained Learning via Structural Latent Parameterization#1118227
- EvoMO-SR: Multiobjective LLM-based Evolution of Symbolic Expressions with substructure guidance#1131029
Record · ID 1133046
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