ABC likelihood-free methods for model choice in Gibbs random fields
From MaRDI portal
Publication:5962444
DOI10.1214/09-BA412zbMath1330.62126arXiv0807.2767WikidataQ60461489 ScholiaQ60461489MaRDI QIDQ5962444
Jean-Michel Marin, Christian P. Robert Robert, Aude Grelaud, Jean-François Taly, François Rodolphe
Publication date: 12 February 2016
Published in: Bayesian Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0807.2767
Random fields; image analysis (62M40) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)
Related Items (28)
ABC shadow algorithm: a tool for statistical analysis of spatial patterns ⋮ Approximate computations for binary Markov random fields and their use in Bayesian models ⋮ Optimal Bayesian design for model discrimination via classification ⋮ A transdimensional approximate Bayesian computation using the pseudo-marginal approach for model choice ⋮ Approximate maximum likelihood estimation of the autologistic model ⋮ Bayesian inference on the memory parameter for gamma-modulated regression models ⋮ An approximate likelihood perspective on ABC methods ⋮ Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution ⋮ Delayed Acceptance ABC-SMC ⋮ Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets ⋮ Semi-automatic selection of summary statistics for ABC model choice ⋮ Multilevel rejection sampling for approximate Bayesian computation ⋮ Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology ⋮ Likelihood-free Bayesian inference for \(\alpha\)-stable models ⋮ A synthetic likelihood approach for intractable Markov random fields ⋮ Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes ⋮ Approximate Bayesian computations to fit and compare insurance loss models ⋮ Bayesian model comparison with un-normalised likelihoods ⋮ Pre-processing for approximate Bayesian computation in image analysis ⋮ An external field prior for the hidden Potts model with application to cone-beam computed tomography ⋮ Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields ⋮ AABC: approximate approximate Bayesian computation for inference in population-genetic models ⋮ Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo ⋮ Diagnostic tools for approximate Bayesian computation using the coverage property ⋮ Quantification of the weight of fingerprint evidence using a ROC-based approximate Bayesian computation algorithm for model selection ⋮ Scalable Bayesian inference for the inverse temperature of a hidden Potts model ⋮ Markov random field models for vector-based representations of landscapes ⋮ A Note on Bayesian Inference for Long-Range Dependence of a Stationary Two-State Process
This page was built for publication: ABC likelihood-free methods for model choice in Gibbs random fields
