Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images.¶
Geman, S., & Geman, D. (1984). Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Annealing
- Cool too fast (a quench) and the search freezes into whatever local minimum it happened to be near when mobility vanished, refreezing the original defect; cool slowly enough and a classic theorem guarantees convergence to the global optimum in the limit.
This sourceProves that a sufficiently slow logarithmic cooling schedule guarantees convergence of simulated annealing to the global optimum.
- Cool too fast (a quench) and the search freezes into whatever local minimum it happened to be near when mobility vanished, refreezing the original defect; cool slowly enough and a classic theorem guarantees convergence to the global optimum in the limit.
- Markov Blanket
- The construction also licenses Gibbs sampling: to resample \(X\), one needs only its blanket's current values, never the whole graph — the computational payoff of locality.
This sourceEstablishes Gibbs sampling, which resamples a variable from its Markov blanket alone.
- The construction also licenses Gibbs sampling: to resample \(X\), one needs only its blanket's current values, never the whole graph — the computational payoff of locality.
- Monte Carlo Simulation
This sourceClassical convergence-to-global-optimum proof for simulated annealing under logarithmic cooling, framed as Gibbs sampling for Bayesian image restoration; foundational MCMC and SA convergence theory.
- Simulated Annealing
- Geman and Geman (1984)
This sourceClassical convergence-to-global-optimum proof for simulated annealing under logarithmic cooling, framed as Gibbs sampling for Bayesian image restoration; foundational MCMC and SA convergence theory.
- Geman and Geman (1984)
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