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

Hoover

书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods影响因子(影响力)




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods影响因子(影响力)学科排名




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods网络公开度




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods网络公开度学科排名




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods被引频次




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods被引频次学科排名




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods年度引用




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods年度引用学科排名




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods读者反馈




书目名称Image Analysis, Random Fields and Markov Chain Monte Carlo Methods读者反馈学科排名

Cultivate

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.

纤细

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.

实现

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.

嘲笑

Gibbsian Sampling and Annealing RevisitedGibbsian sampling and Gibbsian annealing introduced in Chapter 5 will be generalized in two respects:

Ganglion

誓言

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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