The Bayesian paradigm provides a coherent and unified approach to
problems of statistical inference such as parameter estimation,
hypothesis testing, prediction, or model discrimination within a
decision-theoretic framework.
Bayesian inference for complex models heavily relies on computationally
intensive methods.
At the IFAS we are currently working on Bayesian modelling of
categorical and mixed data, Bayesian estimation of mixture and
treatment effects models, Bayesian model selection and approximate Bayesian
computation for models with intractable likelihoods.
Forschungs-