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My research lies in uncertainty quantification and sampling algorithms, with a focus on Markov chain Monte Carlo (MCMC) methods. I am interested in both theoretical questions and applications, particularly in problems arising from neuroscience and computational biology.
Publications & Preprints
Eriksson, O., Kramer, A., Milinanni, F., & Nyquist, P. (2025). Sensitivity Approximation by the Peano-Baker Series. Numerische Mathematik: 1-50.
Milinanni, F. (2024). Large deviation-based tuning schemes for Metropolis-Hastings algorithms. arXiv:2409.20337.
Milinanni, F., & Nyquist, P. (2024). Large deviations for Independent Metropolis Hastings and Metropolis-adjusted Langevin algorithm. arXiv:2403.08691.
Milinanni, F., & Nyquist, P. (2024). A large deviation principle for the empirical measures of Metropolis–Hastings chains. Stochastic Process. Appl., 170, 104293.
Kramer, A., Milinanni, F., Hellgren Kotaleski, J., Nyquist, P., Jauhiainen, A., & Eriksson, O. (2023). UQSA–An R-Package for Uncertainty Quantification and Sensitivity Analysis for Biochemical Reaction Network Models. arXiv:2308.05527.
Papers in preparation
Milinanni, F. & Yang J. (2026+). On mixing times of Stereographic MCMC for κ-concave target distributions. In preparation.
Eriksson O., García-Pareja C., & Milinanni, F. (2026+). On summary statistics for Approximate Bayesian Computation in Biochemical Reaction Network Models with Stochastic Switching Behaviour. In preparation.
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