Posterior Sampling When the Normalizing Constant is Unknown

DOI10.1080/03610918.2011.555042zbMath1216.62043OpenAlexW1963733315MaRDI QIDQ3015884

Stephen G. Walker

Publication date: 13 July 2011

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2011.555042

zbMATH Keywords

Gibbs samplingreversible jump MCMCunknown normalizing constant

Mathematics Subject Classification ID

Related Items (14)

Noise contrastive estimation: asymptotic properties, formal comparison with MC-MLE ⋮ Prior specification of neighbourhood and interaction structure in binary Markov random fields ⋮ Approximate computations for binary Markov random fields and their use in Bayesian models ⋮ Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants ⋮ Exact Bayesian inference for the Bingham distribution ⋮ Modified Pólya-Gamma data augmentation for Bayesian analysis of directional data ⋮ Bayesian inference with misspecified models ⋮ An EPPF from independent sequences of geometric random variables ⋮ A Bayesian analysis of the Bingham distribution ⋮ On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods ⋮ The Poisson transform for unnormalised statistical models ⋮ Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models ⋮ On nonnegative unbiased estimators ⋮ Bayesian Inference in Cumulative Distribution Fields

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Normalized random measures driven by increasing additive processes
• Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables
• Efficient Calculation of the Normalizing Constant of the Autologistic and Related Models on the Cylinder and Lattice
• An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

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