Sona Hunanyan is a scientist-statistician at Philip Morris Armenia. Apart from that, she is working on research projects in statistical analysis of machine learning algorithms within the framework of the FAST Advance STEM program.
Sona completed a Master's Degree in Applied Mathematics at EPFL Lausanne. Beside her studies, she gained practical experience while working as a quantitative analyst at Nuclear Power Plant Goesgen, Switzerland. Sona holds a PhD in Biostatistics from the University of Zurich. The main topic of her PhD thesis was Bayesian hierarchical modeling and software development in R.
The parameters in a statistical model are not always identified by the data. In Bayesian analysis, this
problem remains unnoticed because of prior assumptions. It is crucial to find out whether the data
determine the marginal posterior parameters. As the famous mathematician George Box stated,
“Since all models are wrong the scientist must be alert to what is importantly wrong.”
The R package ed4bhm, which is available on GitHub allows to examine the empirical determinacy of
posterior parameters for the models fitted with well-known Bayesian techniques.