Abstract
This chapter introduces Metropolis type algorithms which are popular alternatives to the Gibbsian versions considered previously. For low temperature and many states these methods usually are preferable. Metropolis methods are not restricted to product spaces and therefore lend themselves to many applications outside imaging, for example in combinatorial optimization. Related and more general samplers will be described as well.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Winkler, G. (1995). Metropolis Algorithms. In: Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Applications of Mathematics, vol 27. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-97522-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-97522-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-97524-0
Online ISBN: 978-3-642-97522-6
eBook Packages: Springer Book Archive