Lunds universitet

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Research interests: Bayesian inference, intractable likelihood problems, and Monte Carlo methods.


S. Wiqvist, A. Golightly, AT Mclean, U. Picchini (2019). Efficient inference for stochastic differential mixed-effects models using correlated particle pseudo-marginal algorithms, arXiv:1907.09851.

S. Wiqvist, U. Picchini and J. Forman (2018). Accelerating delayed-acceptance Markov chain Monte Carlo algorithms, arXiv:1806.05982.

Conference papers:

S. Wiqvist, P-A. Mattei, U. Picchini and J. Frellsen (2019). Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian Computation. Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6798--6807.