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Titlebook: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods; A Mathematical Intro Gerhard Winkler Book 2003Latest edition Springer-V

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書目名稱Image Analysis, Random Fields and Markov Chain Monte Carlo Methods
副標(biāo)題A Mathematical Intro
編輯Gerhard Winkler
視頻videohttp://file.papertrans.cn/462/461409/461409.mp4
概述Includes supplementary material:
叢書名稱Stochastic Modelling and Applied Probability
圖書封面Titlebook: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods; A Mathematical Intro Gerhard Winkler Book 2003Latest edition Springer-V
描述This second edition of G. Winkler‘s successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required..The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added.
出版日期Book 2003Latest edition
關(guān)鍵詞Bayesian statistics; Estimator; Likelihood; Markov chain Monte Carlo methods; Partition; Random variable;
版次2
doihttps://doi.org/10.1007/978-3-642-55760-6
isbn_softcover978-3-642-62911-2
isbn_ebook978-3-642-55760-6Series ISSN 0172-4568 Series E-ISSN 2197-439X
issn_series 0172-4568
copyrightSpringer-Verlag GmbH Germany 2003
The information of publication is updating

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Cleaning Dirty PicturesIn this chapter we will discuss and illustrate the previously introduced concepts. We will pursue the process how expectations and restrictions are translated to and incorporated into Bayesian models.
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Finite Random FieldsWe met a variety of distributions on finite products of finite sets of states. They were all strictly positive and only a small number of neighbour sites interacted. These and other common principles will be formalized in the present chapter and illustrated by further examples. It also contains a collection of basic results on random fields.
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Cooling SchedulesAnnealing with the theoretical cooling schedule may work very slowly. Therefore, in practice faster cooling schedules are adopted. We shall compare the results of such algorithms with exact MAP estimations.
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Gibbsian Sampling and Annealing RevisitedGibbsian sampling and Gibbsian annealing introduced in Chapter 5 will be generalized in two respects:
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Metropolis AlgorithmsThis chapter introduces Metropolis-Hastings type samplers. They generalize the Gibbs sampler and are not dependent upon a product structure of the search space X. Hence they apply in a wide range of applications besides imaging; an important example is combinatorial optimization.
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