Bayesian modelling for asymmetric multi-modal circular data

Kılıç, Muhammet Burak
In this thesis, we propose a Bayesian methodology based on sampling importance re-sampling for asymmetric and bimodal circular data analysis. We adopt Dirichlet process (DP) mixture model approach to analyse multi-modal circular data where the number of components is not known. For the analysis of temporal circular data, such as hourly measured wind directions, we join DP mixture model approach with circular times series modelling. The approaches are illustrated with both simulated and real life data sets. Our Bayesian methodologies have been shown to have good statistical properties in multi-modal circular data analysis. Computational codes for DP mixture models are constructed in OpenBUGS and R.


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Citation Formats
M. B. Kılıç, “Bayesian modelling for asymmetric multi-modal circular data,” Ph.D. - Doctoral Program, Middle East Technical University, 2015.