Simulated annealing algorithms and Markov chains with rare transitions
Olivier Catoni (1999)
Séminaire de probabilités de Strasbourg
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Displaying similar documents to “Simulated annealing”
Simulated annealing algorithms and Markov chains with rare transitions
Hit and run as a unifying device
Hans C. Andersen, Persi Diaconis (2007)
Journal de la société française de statistique
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We present a generalization of hit and run algorithms for Markov chain Monte Carlo problems that is ‘equivalent’ to data augmentation and auxiliary variables. These algorithms contain the Gibbs sampler and Swendsen-Wang block spin dynamics as special cases. The unification allows theorems, examples, and heuristics developed in one domain to illuminate parallel domains.
Asymptotic behaviour of a BIPF algorithm with an improper target
Claudio Asci, Mauro Piccioni (2009)
Kybernetika
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The BIPF algorithm is a Markovian algorithm with the purpose of simulating certain probability distributions supported by contingency tables belonging to hierarchical log-linear models. The updating steps of the algorithm depend only on the required expected marginal tables over the maximal terms of the hierarchical model. Usually these tables are marginals of a positive joint table, in which case it is well known that the algorithm is a blocking Gibbs Sampler. But the algorithm makes...
Simulated annealing on uncorrelated energy landscapes.
Goertzel, Ben, Ananda, Malwane (1994)
International Journal of Mathematics and Mathematical Sciences
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An expectation maximization algorithm to model failure times by continuous-time Markov chains.
Duan, Qihong, Chen, Zhiping, Zhao, Dengfu (2010)
Mathematical Problems in Engineering
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A method for knowledge integration
Martin Janžura, Pavel Boček (1998)
Kybernetika
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With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.