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.
Institute of Applied Statistics
Bayesian statistics and MCMC methods
Johannes Kepler Universität Linz
Altenberger Straße 69
Science Park 2, intermediate storey
+43 732 2468 6801
+43 732 2468 6820