Umberto Picchini

According to our database, Umberto Picchini authored at least 11 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings.

Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini

CoRR, 2024

2023

Jana: Jointly amortized neural approximation of complex Bayesian models.

Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner

Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022

Scalable and flexible inference framework for stochastic dynamic single-cell models.

Sebastian Persson, Niek Welkenhuysen, Sviatlana Shashkova, Samuel Wiqvist, Patrick Reith, Gregor W. Schmidt, Umberto Picchini, Marija Cvijovic

PLoS Comput. Biol., 2022

2021

Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms.

Samuel Wiqvist, Andrew Golightly, Ashleigh T. McLean, Umberto Picchini

Comput. Stat. Data Anal., 2021

Sequential Neural Posterior and Likelihood Approximation.

Samuel Wiqvist, Jes Frellsen, Umberto Picchini

CoRR, 2021

2019

Likelihood-free stochastic approximation EM for inference in complex models.

Umberto Picchini

Commun. Stat. Simul. Comput., 2019

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation.

Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models.

Umberto Picchini, Adeline Samson

Comput. Stat., 2018

2017

Approximate maximum likelihood estimation using data-cloning ABC.

Umberto Picchini, Rachele Anderson

Comput. Stat. Data Anal., 2017

2011

Practical estimation of high dimensional stochastic differential mixed-effects models.

Umberto Picchini, Susanne Ditlevsen

Comput. Stat. Data Anal., 2011

2008

Parameters of the Diffusion Leaky Integrate-and-Fire Neuronal Model for a Slowly Fluctuating Signal.

Umberto Picchini, Susanne Ditlevsen, Andrea De Gaetano, Petr Lánský

Neural Comput., 2008