Variational inference for Dirichlet process mixtures
, 2006 Dirichlet process (DP) mixture models are the cornerstone of non- parametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of non- parametric Bayesian methods to a
Michael I. Jordan, Michael I. Jordan
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Countable representation for infinite-dimensional diffusions derived from the two parameter Poisson Dirichlet process [PDF]
, 2009 This paper provides a countable representation for a class of infinite-dimensional diffusions which extends the infinitely-many-neutral-alleles model and is related to the two-parameter Poisson-Dirichlet process. By means of Gibbs sampling procedures, we
Ruggiero, Matteo +3 more
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IEEE Access, 2019 In this paper, we propose nonparametric hierarchical Bayesian models based on two inverted Dirichlet-based distributions and Pitman-Yor process for positive data features clustering.
Wentao Fan, Nizar Bouguila
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Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations [PDF]
Monthly notices of the Royal Astronomical Society, 2018 We report on the Dirichlet Process Gaussian-mixture model, a fully Bayesian non-parametric method that can be used to estimate probability densities and compute credible volumes with a minimal set of assumptions.
W. Del Pozzo +5 more
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Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations [PDF]
, 2014 We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter (Formula presented.).
Silvia Liverani +5 more
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Semiparametric Bayesian analysis of high-dimensional censored outcome data
Statistical Theory and Related Fields, 2017 The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data for US individuals diagnosed with cancer. Semiparametric Bayesian methods are computationally expensive to fit for such large data-sets.
Chetkar Jha, Yi Li, Subharup Guha
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Memorized Variational Continual Learning for Dirichlet Process Mixtures
IEEE Access, 2019 Bayesian nonparametric models are theoretically suitable for streaming data due to their ability to adapt model complexity with the observed data. However, very limited work has addressed posterior inference in a streaming fashion, and most of the ...
Yang Yang, Bo Chen, Hongwei Liu
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Underground Pipeline Mapping Based on Dirichlet Process Mixture Model
IEEE Access, 2020 Underground pipeline mapping is important in urban construction. There are few specific procedures and approaches to map underground pipelines using ground penetration radar (GPR) without knowing the number of buried pipelines.
Qingyuan Wu, Xiren Zhou, Huanhuan Chen
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Variational methods for the Dirichlet process [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While often less accurate than MCMC, variational methods provide a fast deterministic approximation to marginal and conditional probabilities.
David M. Blei, Michael I. Jordan
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Spiked Dirichlet Process Priors for Gaussian Process Models
Journal of Probability and Statistics, 2010 We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates.
Terrance Savitsky, Marina Vannucci
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