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Bayesian inference of biological networks whose components are linearly dependent
Date
2017-06-01
Author
Purutçuoğlu Gazi, Vilda
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URI
https://hdl.handle.net/11511/85175
Conference Name
International Conference on Progress in Applied Science, (01 Haziran 2017)
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Department of Statistics, Conference / Seminar
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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. ...
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V. Purutçuoğlu Gazi, “Bayesian inference of biological networks whose components are linearly dependent,” presented at the International Conference on Progress in Applied Science, (01 Haziran 2017), İstanbul, Türkiye, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/85175.