Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images

By S. German, D. German. 1988

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1. Huan Liu
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8. Trivellore E. Raghunathan

Citations in the corpus

• A multivariate technique for multiply imputing missing values using a sequence of regression models (2001, methodology): “When the pattern of missing data is not monotone, one can develop a Gibbs sampling algorithm (Geman and Geman 1984; Gelfand and Smith 1990) corresponding to Model (1).”
• Relational learning via latent social dimensions (2009, methodology): “Relaxation labeling [4], iterative classification [18] and Gibbs sampling [9] are the commonly used techniques.”
• Posterior Computation with the Gibbs Zig-Zag Sampler (2020, methodology): “nt information, ranging from Hamiltonian Monte Carlo (HMC, Duane et al., 1987) to Metropolis-adjusted Langevin algorithms (MALA, Roberts and Tweedie, 1996). Likewise, this includes the Gibbs sampler (Geman and Geman, 1987) and generalizations that replace sampling parameters one at a time from their”
• Learning Spatial Context: Using Stuff to Find Things (2008, methodology): “We solve the inference problem approximately using a Gibbs sampling [19] MCMC method.”
• Hierarchical Bayesian models for inverse problems in heat conduction (2005, background): “(Some figures in this article are in colour only in the electronic version)”