Extra Seminar Mathematics - Dr. R.Jacobs
|When:||Th 07-03-2019 09:00 - 09:45|
Bayesian variable selection and non-linear mixed models for complex data in health research
In this era of automated systems, datasets are becoming increasingly complex: many (correlated) variables, multilevel structures, multiple outcomes, measurement error, missing values. Such big complex data provide new opportunities for health researchers to answer increasingly more complex research questions, for instance related to early detection and prevention of diseases and advancement of healthy ageing. To be able to answer these increasingly more complex research questions, we need an inter-disciplinary research mentality and a sound methodological foundation. In my research, I combine these two aspects by developing Bayesian methodology. Bayesian methodology provides a framework for integrating different disciplines. The informative prior distributions used for this integration also provide the means of modelling the complex relations in health data. In this presentation, I will discuss some of my research on Bayesian methodology. I will start by presenting a few examples from health research which motivate the development of sophisticated Bayesian methodology. These include some of my current and future research activities. Then I will zoom into Bayesian variable selection and discuss a recent research project on Bayesian variable selection methodology for food-borne disease outbreaks.