2019-01-18

Subhadip Pal, Ph.D., Department of Bioinformatics and Biostatistics, University of Louisville

"Data Augmentation Algorithms for Bayesian Analysis of Directional Data"

Novel data-augmentation algorithms are proposed for fully Bayesian analysis of directional data in arbitrary dimension. The approach leads to novel classes of distributions, which are constructed in detail. The proposed data-augmentation strategies lead to methods for the posterior inference that circumvent the need for analytic approximations to integration, numerical integration, or Metropolis-Hastings. Simulations and real data examples are presented to demonstrate the applicability and to perceive the performance of the procedure.

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