Abstract: Novel data augmentation algorithms are proposed for Bayesian analysis of the directional data in arbitrary dimensions. The approach leads to new classes of distributions which are constructed in detail. The proposed data augmentation strategies circumvent the need for analytic approximations to integration, numerical integration, or Metropolis-Hastings for the corresponding posterior inference. Simulations and real data examples are presented to demonstrate the applicability and to apprise the performance of the procedure.

 

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