Last modified by mhashemi on 2025/05/09 17:29
From version 10.2
edited by mhashemi
on 2024/12/03 16:24
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To version 9.1
edited by mhashemi
on 2024/11/27 17:47
Change comment: There is no comment for this version

Summary

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10 10  
11 11  )))
12 12  
13 -This tool was developed at INS in Marseille.
14 -Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
15 -
16 -
17 17  The aim is to provide inference services for Dynamical Causal Modeling of Event-Related Potentials (ERPs) measured with EEG/MEG, using SATO Probabilistic Programming Languages (PPLs):
18 18  
19 19  Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]]
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23 23  PyMC: [[https:~~/~~/www.pymc.io/welcome.html>>url:https://www.pymc.io/welcome.html]]
24 24  
25 25  Stan: [[https:~~/~~/mc-stan.org/>>url:https://mc-stan.org/]]
26 -
27 -We have provided a taxonomy for model comparison tailored to algorithms: (1) adaptive Hamiltonian Monte Carlo, (2) automatic Laplace and (3) family of variational inference. We have provided solutions to address the deference by: 1) optimizing the hyperparameters, (2) leveraging initialization with prior information, (3) weighted stacking based on predictive accuracy.
28 -
29 -
30 -Notebooks:
22 +\\\\\\Notebooks:
31 31  \\[[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/DCM_ERP_NumPyro>>https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/DCM_ERP_NumPyro]]
32 -\\Tutorial:
24 +\\
33 33  
34 -[[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/EITN_tutorial>>https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/EITN_tutorial]]
35 -
36 36  {{{@article{Baldy2024AutoDCM,
37 37   title={Dynamic Causal Modeling in Probabilistic Programming Languages},
38 38   author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam},