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Department of Public Health Sciences


BMTRY 719 : Bayesian Biostatistics


It is a graduate course on effective and sophisticated approaches to Bayesian modeling and computation in biostatistics and related fields. The course begins with a gentle introduction of Bayesian inference starting from first principle, but it intends to cover the philosophical backgrounds, logical developments and computational tools associated with Bayesian


  • Bayes rule
  • Bayesian inference for single parameter model
  • Multiparameter models
  • Connection to classical statistics
  • Scopes of Bayesian statistics in medicine
  • Hierarchical models in biostatistics
  • Model checking and sensitivity analysis 
  • Study design in Bayesian analysis
  • Bayesian regression
  • Computation and simulation
  • Posterior simulation
  • Hierarchical linear models from Bayesian perspective
  • Bayesian Generalized linear model
  • Multivariate models in Bayesian analysis 
  • Bayesian mixture models
  • Missing data in Bayesian statistics


BMTRY 700, 701, 706, 707.  Typically offered every other fall semester.


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