Article
One-sample Bayes inference for symmetric distributions of 3-D rotations
Computational Statistics & Data Analysis
Document Type
Article
Disciplines
Publication Version
Accepted Manuscript
Publication Date
1-1-2014
DOI
10.1016/j.csda.2013.02.004
Abstract
A variety of existing symmetric parametric models for 3-D rotations found in both statistical and materials science literatures are considered from the point of view of the “uniform-axis-random-spin” (UARS) construction. One-sample Bayes methods for non-informative priors are provided for all of these models and attractive frequentist properties for corresponding Bayes inference on the model parameters are confirmed. Taken together with earlier work, the broad efficacy of non-informative Bayes inference for symmetric distributions on 3-D rotations is conclusively demonstrated.
Rights
© 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Creative Commons License
Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International
Copyright Owner
Elsevier, B.V.
Copyright Date
2014
Language
en
File Format
application/pdf
Citation Information
Yu Qiu, Danial J. Nordman and Stephen B. Vardeman. "One-sample Bayes inference for symmetric distributions of 3-D rotations" Computational Statistics & Data Analysis Vol. 71 (2014) p. 520 - 529 Available at: http://works.bepress.com/stephen_vardeman/20/
This is a manuscript of an article published as One-sample Bayes inference for existing symmetric distributions on 3-d rotations. Computational Statistics and Data Analysis, 2014, Vol. 71, pp. 520-529, DOI:10.1016/j.csda.2013.02.004. With Yu Qiu and Dan Nordman.