ArticleBayesian analysis2019
Latent Nested Nonparametric Priors (with Discussion).
Article in Bayesian analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Enriched Pitman-Yor processes.Scandinavian journal of statistics, theory and applications · 2025Article
- Immune Profiling among Colorectal Cancer Subtypes using Dependent Mixture Models.Journal of the American Statistical Association · 2025Article
- Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.Biometrics · 2024Article
- Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data.Journal of machine learning research : JMLR · 2024Article
- HOW CLOSE AND HOW MUCH? LINKING HEALTH OUTCOMES TO BUILT ENVIRONMENT SPATIAL DISTRIBUTIONS.The annals of applied statistics · 2023Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data.Journal of the American Statistical Association · 2023Article
- Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials.Journal of the American Statistical Association · 2023Article
- Latent Nested Nonparametric Priors (with Discussion).Bayesian analysis · 2019Article
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Authors and funding
5 authors.
Funding
Abstract
Discrete random structures are important tools in Bayesian nonparametrics and the resulting models have proven effective in density estimation, clustering, topic modeling and prediction, among others. In this paper, we consider nested processes and study the dependence structures they induce. Dependence ranges between homogeneity, corresponding to full exchangeability, and maximum heterogeneity, corresponding to (unconditional) independence across samples. The popular nested Dirichlet process is shown to degenerate to the fully exchangeable case when there are ties across samples at the observed or latent level. To overcome this drawback, inherent to nesting general discrete random measures, we introduce a novel class of latent nested processes. These are obtained by adding common and group-specific completely random measures and, then, normalizing to yield dependent random probability measures. We provide results on the partition distributions induced by latent nested processes, and develop a Markov Chain Monte Carlo sampler for Bayesian inferences. A test for distributional homogeneity across groups is obtained as a by-product. The results and their inferential implications are showcased on synthetic and real data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.