PhD students

    2017-ongoing: Samuel Wiqvist (Lund University), working on inference for intractable likelihoods, especially for state space stochastic differential equation models, using particles-based methods (sequential Monte Carlo), Gaussian processes and other likelihood-free methods. Applications are considering protein folding data, see this description. Samuel's research is funded by a grant from the Swedish National Research Council.
    2014-15: Rachele Anderson, Lund University.

    Master's students

    2017: David Zenkert, No-show Forecast Using Passenger Booking Data. Lund University, Sweden.
    2015: Danial Ali Akbari, Maximum likelihood estimation using Bayesian Monte Carlo methods. Lund University, Sweden.
    2013: Oskar Nilsson, Likelihood-free inference and approximate Bayesian computation for stochastic modelling, Lund University, Sweden.
    2012: Angela Ciliberti, Parametric inference for stochastic differential equations, Lund University, Sweden.
    2011: Alexander Powne, "Diagnostic measures for generalized linear models", Durham University, UK.

    Bachelor's students

    2017: Sara Bengtsson, Risk based monitoring in clinical studies - improving data quality. Lund University, Sweden.
    2017: Annika Israelsson, Statistical inference of pharmacokinetic models of Theophylline and Warfarin Data. Lund University, Sweden.
    2017: Rasmus HallÚn, A study of gradient-based algorithms. Lund University, Sweden.