Last modified by mhashemi on 2025/05/09 17:29
From version 13.2
edited by mhashemi
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To version 9.1
edited by mhashemi
on 2024/11/27 17:47
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... ... @@ -10,14 +10,8 @@
10 10  
11 11  )))
12 12  
13 -This tool called DCM_PPLs, was developed at INS in Marseille.
13 +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):
14 14  
15 -
16 -Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
17 -
18 -
19 -The aim was to provide inference services for Dynamical Causal Modeling of Event-Related Potentials (ERPs) measured with EEG/MEG, using SATO Probabilistic Programming Languages (PPLs):
20 -
21 21  Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]]
22 22  
23 23  Blackjax: [[https:~~/~~/blackjax-devs.github.io/blackjax/>>url:https://blackjax-devs.github.io/blackjax/]]
... ... @@ -25,21 +25,11 @@
25 25  PyMC: [[https:~~/~~/www.pymc.io/welcome.html>>url:https://www.pymc.io/welcome.html]]
26 26  
27 27  Stan: [[https:~~/~~/mc-stan.org/>>url:https://mc-stan.org/]]
28 -
29 -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.
30 -
31 -
32 -Github: [[https:~~/~~/github.com/ins-amu/DCM_PPLs>>https://github.com/ins-amu/DCM_PPLs]]
33 -
34 -
35 -Notebooks:
22 +\\\\\\Notebooks:
36 36  \\[[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]]
37 -\\Tutorial:
24 +\\
38 38  
39 -[[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]]
40 -
41 -
42 -{{{@article{AutoDCM,
26 +{{{@article{Baldy2024AutoDCM,
43 43   title={Dynamic Causal Modeling in Probabilistic Programming Languages},
44 44   author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam},
45 45   journal={bioRxiv},