Research

Pre-prints

  1. Persson, S., Welkenhuysen, N., Shashkova, S., Wiqvist, S., Reith, P., W Schmidt, G., Picchini, U., & Cvijovic, M. (2021). PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models. bioRxiv preprint bioRxiv:2021.07.01.450748.
    [bioRxiv][Code]

  2. Wiqvist, S., Frellsen, J., & Picchini, U. (2021). Sequential Neural Posterior and Likelihood Approximation. arXiv preprint arXiv:2102.06522.
    [arXiv][Code]

  3. Wiqvist, S., Picchini, U., Forman, J. L., Lindorff-Larsen, K., & Boomsma, W. (2018). Accelerating delayed-acceptance Markov chain Monte Carlo algorithms. arXiv preprint arXiv:1806.05982.
    [arXiv][Code]

Peer-reviewed publications

  1. Wiqvist, S., Golightly, A., McLean, A. T., & Picchini, U. (2021). Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms. Computational Statistics & Data Analysis, 157, 107151.
    [Paper][arXiv][Code]

  2. Wiqvist, S., Mattei, P. A., Picchini, U., & Frellsen, J. (2019). Partially exchangeable networks and architectures for learning summary statistics in approximate Bayesian computation. In International Conference on Machine Learning (pp. 6798-6807). PMLR.
    [Paper][arXiv][Code][Blog post (by Xi’an)]