Bayesian model-based tight clustering for time course data

DOI10.1007/s00180-009-0159-7zbMath1223.62102OpenAlexW2139387947WikidataQ33892411 ScholiaQ33892411MaRDI QIDQ626248

Youngsung Joo, George Casella, James P. Hobert

Publication date: 22 February 2011

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3087980

zbMATH Keywords

smoothing splinesMarkov chain Monte CarloCrowley's prior

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (1)

Bayesian hierarchical mixture models for detecting non‐normal clusters applied to noisy genomic and environmental datasets

Uses Software

• SemiPar
• AS 136

Cites Work

• Unnamed Item
• Algorithm AS 136: A K-Means Clustering Algorithm
• Standard errors of fitted component means of normal mixture
• Some Probabilistic Aspects of Set Partitions
• Clustering Using Objective Functions and Stochastic Search
• Product Partition Models for Normal Means
• Clustering for Sparsely Sampled Functional Data
• Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data

This page was built for publication: Bayesian model-based tight clustering for time course data