arXiv (All)
open access
Design-Conditional Prior Elicitation for Dirichlet Process Mixtures: A Unified Framework for Cluster Counts and Weight Control
Dirichlet process mixtures describe variation in effects or latent traits across sites, studies, or examinees. The concentration parameter controls the number and relative sizes of the clusters, but its hyperprior is often chosen by default. We describe a method for choosing this hyperprior when the
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
- Variational Inference for Functional Data Clustering via Dirichlet Process Mixtures with Correlated Errors#971517
- Calibrated Bayes analysis of cluster-randomized trials#670432
- Calibrated Bayes analysis of cluster-randomized trials#668367
- On Computing Total Variation Distance Between Mixtures of Product Distributions#155292
- Official Review · ICLR 2026#787059
Record · ID 973060
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