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2020 – today
- 2024
[j8]Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini:
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Trans. Mach. Learn. Res. 2024 (2024)- [i4]Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini:
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. CoRR abs/2403.07454 (2024) - 2023
- [c2]Stefan T. Radev, Marvin Schmitt, Valentin Pratz
, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner:
Jana: Jointly amortized neural approximation of complex Bayesian models. UAI 2023: 1695-1706 - [i3]Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner:
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models. CoRR abs/2302.09125 (2023) - 2022
- [j7]Sebastian Persson, Niek Welkenhuysen, Sviatlana Shashkova, Samuel Wiqvist, Patrick Reith, Gregor W. Schmidt, Umberto Picchini, Marija Cvijovic:
Scalable and flexible inference framework for stochastic dynamic single-cell models. PLoS Comput. Biol. 18(5) (2022) - 2021
- [j6]Samuel Wiqvist, Andrew Golightly, Ashleigh T. McLean, Umberto Picchini:
Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms. Comput. Stat. Data Anal. 157: 107151 (2021) - [i2]Samuel Wiqvist, Jes Frellsen, Umberto Picchini:
Sequential Neural Posterior and Likelihood Approximation. CoRR abs/2102.06522 (2021)
2010 – 2019
- 2019
- [j5]Umberto Picchini:
Likelihood-free stochastic approximation EM for inference in complex models. Commun. Stat. Simul. Comput. 48(3): 861-881 (2019) - [c1]Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen:
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. ICML 2019: 6798-6807 - [i1]Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen:
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. CoRR abs/1901.10230 (2019) - 2018
- [j4]Umberto Picchini, Adeline Samson:
Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models. Comput. Stat. 33(1): 179-212 (2018) - 2017
- [j3]Umberto Picchini, Rachele Anderson:
Approximate maximum likelihood estimation using data-cloning ABC. Comput. Stat. Data Anal. 105: 166-183 (2017) - 2011
- [j2]Umberto Picchini, Susanne Ditlevsen:
Practical estimation of high dimensional stochastic differential mixed-effects models. Comput. Stat. Data Anal. 55(3): 1426-1444 (2011)
2000 – 2009
- 2008
- [j1]Umberto Picchini, Susanne Ditlevsen, Andrea De Gaetano, Petr Lánský:
Parameters of the Diffusion Leaky Integrate-and-Fire Neuronal Model for a Slowly Fluctuating Signal. Neural Comput. 20(11): 2696-2714 (2008)
Coauthor Index
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