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Reference analysis of non-regular models and nonparametric Bayes modeling of large data
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Bayesian analysis is a principled approach, which makes inference about the parameter, by combining the information gained from the data and the prior ...
Scalable Bayesian nonparametric learning for biomedical big data
(University of Missouri--Columbia, 2018)
of inference approaches that extend parametric Bayesian models using in finite dimensional distributions. In this dissertation, novel statistical methods based on Bayesian nonparametric models and their extensions are developed to handle 'omics data for various...
Bayesian non-parametric methods for benefit-risk assessment and massive multiple-domain data
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The development of systematic and structured approaches to assess benefit-risk of medical products is a major challenge for regulatory decision makers. ...