arXiv (All)
open access
Bayesian Tensor Regression for Neuroimaging Data
Multidimensional array data, or tensors, arise naturally in neuroimaging and other high-dimensional applications. We propose a parsimonious Bayesian tensor regression model for studies in which a brain image is the response and predictors are vector-valued covariates. The method extends Bayesian env
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
- AI/Data Science Chen Camp 22#1170235
- Tensor Orthogonal Subspace Split: Theory and Applications#798277
- Tensor Orthogonal Subspace Split: Theory and Applications#796864
- Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis.#209681
- Independent Polygenic Component Scores Link Multivariate Brain Imaging Genetics to Diverse Phenotypes#18854
Record · ID 1064424
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