938 resultados para Chaîne de Markov cachée


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"IEPA/BOW/02-021."--Cover.

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"Critical Trends Assessment Program."--Cover.

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Prepared for the Cache River Joint Venture Partnership (JVP): Illinois Department of Natural Resources, The Nature Conservancy, U.S. Fish and Wildlife Service, Ducks Unlimited, Natural Resources Conservation Service.

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This is a product of the Critical Trends Assessment Program (CTAP) and the Ecosystems Program of the Illinois Dept. of Natural Resources (DNR). Both are funded largely through Conservation 2000, a six-year State of Illinois initiative to enhance nature protection and outdoor recreation by reversing the decline of the state's ecosystems. Conservation 2000 is the culmination of recommendations from CTAP, the Illinois Conservation Congress, and Governor Edgar's Water Resources Land Use Priorities Task Force.--T.p. verso.

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"July 1997."

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On cover: AD719413.

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"Extrait du Bulletin de la Société d'histoire naturelle d'Autun, tome 7. (année 1895)"

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"Als anhang zur Geschichte des osmanischen reichs." "Türkische quellen," p. 7-11.

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On cover: U.S. Department of the Interior, Geological Survey, Conservation Division; U.S. Department of Agriculture, Forest Service, Bridger-Teton National Forest.

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Thesis (Master's)--University of Washington, 2016-06

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Thesis (Ph.D.)--University of Washington, 2016-06

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A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis-Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.