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Bayesian Hierarchical Alignment Methods

  • Kanti V. Mardia
  • Vysaul B. Nyirongo
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

This chapter considers the problem of matching configurations of biological macromolecules when both alignment and superposition transformations are unknown. Alignment denotes correspondence – a bijection or mapping – between points in different structures according to some objectives or constraints. Superposition denotes rigid-body transformations, consisting of translations and rotations, that bring whole or partial configurations together, typically in Euclidean space, as closely as possible and according to some objectives or constraints.

Keywords

Root Mean Square Deviation Shape Analysis Transformation Parameter Acceptance Probability Procrustes Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We are thankful to Professor Peter Green and Dr. Yann Ruffieux for many useful discussions on ALIBI based approaches. Our thanks also go to Chris Fallaize and Zhengzheng Zhang for their helpful comments.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Statistics, School of MathematicsThe University of LeedsLeedsUK
  2. 2.Statistics Division, United NationsNew YorkUSA

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