Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Rodrigues, A. L. C."'
Autor:
Linhart, Julia, Cardoso, Gabriel Victorino, Gramfort, Alexandre, Corff, Sylvain Le, Rodrigues, Pedro L. C.
Determining which parameters of a non-linear model best describe a set of experimental data is a fundamental problem in science and it has gained much traction lately with the rise of complex large-scale simulators. The likelihood of such models is t
Externí odkaz:
http://arxiv.org/abs/2404.07593
Autor:
Häggström, Henrik, Rodrigues, Pedro L. C., Oudoumanessah, Geoffroy, Forbes, Florence, Picchini, Umberto
Publikováno v:
Transactions on Machine Learning Research 2024, https://openreview.net/forum?id=Q0nzpRcwWn
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators. These approaches are collectively known as "simulation-based inference" (SBI). Recent SBI methods have
Externí odkaz:
http://arxiv.org/abs/2403.07454
Many recent works in simulation-based inference (SBI) rely on deep generative models to approximate complex, high-dimensional posterior distributions. However, evaluating whether or not these approximations can be trusted remains a challenge. Most ap
Externí odkaz:
http://arxiv.org/abs/2306.03580
Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary complex and high-dimensional data distributions. The development of appr
Externí odkaz:
http://arxiv.org/abs/2211.09602
Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e. when distinct sets of parameters yield identic
Externí odkaz:
http://arxiv.org/abs/2102.06477
There has been an increasing interest from the scientific community in using likelihood-free inference (LFI) to determine which parameters of a given simulator model could best describe a set of experimental data. Despite exciting recent results and
Externí odkaz:
http://arxiv.org/abs/2012.02807
Autor:
Tolley, Nicholas1 (AUTHOR) nicholas_tolley@brown.edu, Rodrigues, Pedro L. C.2 (AUTHOR), Gramfort, Alexandre3 (AUTHOR), Jones, Stephanie R.1 (AUTHOR)
Publikováno v:
PLoS Computational Biology. 2/26/2024, Vol. 20 Issue 2, p1-29. 29p.
Autor:
Rodrigues, Gelson L. C.1,2 (AUTHOR), Oliveira, Tainara G. de2 (AUTHOR), Gusmão, Suziete B. S.2 (AUTHOR), Ferreira, Odair P.3 (AUTHOR), Vasconcelos, Thiago L.4 (AUTHOR), Guerra, Yuset5 (AUTHOR), Milani, Raquel6 (AUTHOR), Peña-Garcia, Ramón2,7 (AUTHOR), Viana, Bartolomeu C.2,5 (AUTHOR) bartolomeu@ufpi.edu.br
Publikováno v:
Materials (1996-1944). Mar2023, Vol. 16 Issue 5, p1842. 22p.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.