arXiv (All)
open access
Similarity-based Random Partition Distribution for Clustering Functional Data
Random partition distribution is a crucial tool for model-based clustering. This study advances the field of random partition in the context of functional spatial data, focusing on the challenges posed by hourly population data across various regions and dates. We propose an extension of the general
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
- Prediction-powered inference for time series across space#1218507
- Prediction-powered inference for time series across space#1210289
- Modeling inhomogeneous spatial point configurations with applications to replicated patterns in real-life waiting crowds#1357198
- Adaptive Functional Clustering with Structured Dependence via Variational Inference#798259
- Adaptive Functional Clustering with Structured Dependence via Variational Inference#796846
Record · ID 1344405
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