2017-ongoing: I am main supervisor for Samuel Wiqvist (Lund University). He is working on inference for intractable likelihoods, especially for state-space stochastic differential equation models, using particles-based methods (sequential Monte Carlo), approximate Bayesian computation and deep learning. Applications are considering protein folding data, see this description. Samuel's research is funded by a grant from the Swedish National Research Council.
His preprints can be found on arXiv.
2019: Andrea Krogdal. Delayed-acceptance approximate Bayesian computation Markov chain Monte Carlo: faster simulation using a surrogate model. University of Gothenburg, Sweden.
2019: Simon Berglund Watanabe. Identifiability of parameters in PBPK models: identifiability analysis using the profile likelihood method for model parameters in physiologically based pharmacokinetic models. Chalmers University of Technology, Sweden.
2018: Filip Wikman. Approximate Bayesian computation with sequential surrogate likelihoods. Chalmers University of Technology, Sweden.
2018: Marcus Olausson, Prediction of conversion rates in online marketing. Lund University, Sweden.
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.
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 Hallén, A study of gradient-based algorithms. Lund University, Sweden.