Partitioning gene expression data by data-driven Markov chain Monte Carlo
From MaRDI portal
Publication:5138066
DOI10.1080/02664763.2015.1092113OpenAlexW2276172459MaRDI QIDQ5138066
Erlandson F. Saraiva, Francisco Louzada, Adriano K. Suzuki, Luis Aparecido Milan
Publication date: 3 December 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2015.1092113
Related Items
Interpoint distance tests for high-dimensional comparison studies ⋮ A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling ⋮ A Bayesian sparse finite mixture model for clustering data from a heterogeneous population
Cites Work
- Unnamed Item
- Unnamed Item
- Multiple hypothesis testing and clustering with mixtures of non-central \(t\)-distributions applied in microarray data analysis
- Gibbs sampling based Bayesian analysis of mixtures with unknown number of components
- Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
- Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
- A Bayesian analysis of some nonparametric problems
- Clustering Gene Expression Data using a Posterior Split-Merge-Birth Procedure
- A New Convex Hull Algorithm for Planar Sets
- Dealing With Label Switching in Mixture Models
- Computational and Inferential Difficulties with Mixture Posterior Distributions
- Bayesian Measures of Model Complexity and Fit
- Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
- Bayesian Density Estimation and Inference Using Mixtures
- A Bayesian Mixture Model for Partitioning Gene Expression Data
This page was built for publication: Partitioning gene expression data by data-driven Markov chain Monte Carlo