Scalable Bayesian inference for the inverse temperature of a hidden Potts model

DOI10.1214/18-BA1130zbMath1437.62324arXiv1503.08066OpenAlexW2963432821WikidataQ62554048 ScholiaQ62554048MaRDI QIDQ2297228

Anthony N. Pettitt, Geoff K. Nicholls, Matthew T. Moores, Kerrie L. Mengersen

Publication date: 18 February 2020

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1503.08066

zbMATH Keywords

image analysisexchange algorithmapproximate Bayesian computationhidden Markov random fieldindirect inferenceintractable likelihood

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Bayesian inference (62F15) Image analysis in multivariate analysis (62H35) Markov processes: estimation; hidden Markov models (62M05)

Related Items (8)

The node-wise pseudo-marginal method: model selection with spatial dependence on latent graphs ⋮ Improving detection of changepoints in short and noisy time series with local correlations: connecting the events in pixel neighbourhoods ⋮ Neural networks for parameter estimation in intractable models ⋮ A synthetic likelihood approach for intractable Markov random fields ⋮ Efficient MCMC for Gibbs random fields using pre-computation ⋮ Scalable Bayesian inference for the inverse temperature of a hidden Potts model ⋮ bayesImageS ⋮ Gibbs posterior convergence and the thermodynamic formalism

Uses Software

• R
• RcppArmadillo
• Stan
• GPS-ABC
• abcrf
• PottsUtils

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