One-sample Bayes inference for symmetric distributions of 3-D rotations
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.
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.