Nested annealing: a provable . . .
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Nested Annealing: A Provable Improvement to Simulated Annealing
Simulated Annealing is a family of randomized algorithms for solving multivariate global optimization problems. Empirical results from the application of Simulated Annealing algorithms to certain hard problems including certain types of NPcomplete problems demonstrate that these algorithms yield better results than known heuristic algorithms. But for the worst case input, the time bound can be ...
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Simulated Annealing is a family of randomized algorithms used to solve many combinatorial optimization problems. In practice they have been applied to solve some presumably hard (e.g., NP-complete) problems. The level of performance obtained has been promising [5, 2, 6, 14]. The success of this heuristic technique has motivated analysis of this algorithm from a theoretical point of view. In par...
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Nested sampling estimates directly how the likelihood function relates to prior mass. The evidence (alternatively the marginal likelihood, marginal density of the data, or the prior predictive) is immediately obtained by summation. It is the prime result of the computation, and is accompanied by an estimate of numerical uncertainty. Samples from the posterior distribution are an optional byprod...
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Simulated Annealing is a family of randomized algorithms used to solve many combinatorial optimization problems. In practice they have been applied to solve some presumably hard (e.g., NP-complete) problems. The level of performance obtained has been promised [5, 2, 6, 14]. The success of its heuristic technique has motivated analysis of this algorithm from a theoretical point of view. In parti...
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