Comparing consensus Monte Carlo strategies for distributed Bayesian computation

DOI10.1214/17-BJPS365zbMath1385.65008OpenAlexW2774971100MaRDI QIDQ1705538

Steven L. Scott

Publication date: 16 March 2018

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/17-bjps365

zbMATH Keywords

big dataBayesian modelingcloud computing

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (7)

Parallel Markov chain Monte Carlo for Bayesian hierarchical models with big data, in two stages ⋮ Global Consensus Monte Carlo ⋮ Distributed computation for marginal likelihood based model choice ⋮ Divide-and-conquer Metropolis-Hastings samplers with matched samples ⋮ Comment: Consensus Monte Carlo using expectation propagation ⋮ Unnamed Item ⋮ Asymptotic properties of parallel Bayesian kernel density estimators

Uses Software

• parallelMCMCcombine
• Bigtable

Cites Work

• Unnamed Item
• Unnamed Item
• Asymptotic Behaviour of the Posterior Distribution in Overfitted Mixture Models
• Modelling high-dimensional data by mixtures of factor analyzers
• Variational approximations in Bayesian model selection for finite mixture distributions
• Sequential Monte Carlo Methods in Practice
• The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC
• Bayesian Variable Selection in Clustering High-Dimensional Data
• Variational inference for Dirichlet process mixtures

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