Bayesian inference for latent biologic structure with determinantal point processes (DPP)

DOI10.1111/biom.12482zbMath1390.62320arXiv1506.08253OpenAlexW3123160426WikidataQ40000208 ScholiaQ40000208MaRDI QIDQ141279

Yanxun Xu, Donatello Telesca, Peter Müller, Peter Mueller, Yanxun Xu, Donatello Telesca

Publication date: 12 February 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.08253

zbMATH Keywords

reversible jump Markov chain Monte Carlodeterminantal point processlatent structurebiomedicalrepulsive

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (7)

MCMC Computations for Bayesian Mixture Models Using Repulsive Point Processes ⋮ On a class of repulsive mixture models ⋮ Bayesian Repulsive Gaussian Mixture Model ⋮ dppmix ⋮ Density regression using repulsive distributions ⋮ Adaptive Bayesian nonparametric regression using a kernel mixture of polynomials with application to partial linear models ⋮ Determinantal point process mixtures via spectral density approach

Uses Software

• BrainWeb

Cites Work

• Asymptotic Behaviour of the Posterior Distribution in Overfitted Mixture Models
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Posterior contraction rates of the phylogenetic Indian buffet processes
• Feature allocations, probability functions, and paintboxes
• Determinantal Point Processes for Machine Learning
• The coincidence approach to stochastic point processes
• MAD Bayes for Tumor Heterogeneity—Feature Allocation With Exponential Family Sampling
• Cluster and feature modeling from combinatorial stochastic processes
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