Last modified by mhashemi on 2025/05/09 17:29
From version 9.1
edited by mhashemi
on 2024/11/27 17:47
Change comment: There is no comment for this version
To version 13.2
edited by mhashemi
on 2025/05/09 17:20
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -10,8 +10,14 @@
10 10  
11 11  )))
12 12  
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):
13 +This tool called DCM_PPLs, was developed at INS in Marseille.
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 +
15 15  Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]]
16 16  
17 17  Blackjax: [[https:~~/~~/blackjax-devs.github.io/blackjax/>>url:https://blackjax-devs.github.io/blackjax/]]
... ... @@ -19,11 +19,21 @@
19 19  PyMC: [[https:~~/~~/www.pymc.io/welcome.html>>url:https://www.pymc.io/welcome.html]]
20 20  
21 21  Stan: [[https:~~/~~/mc-stan.org/>>url:https://mc-stan.org/]]
22 -\\\\\\Notebooks:
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:
23 23  \\[[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]]
24 -\\
37 +\\Tutorial:
25 25  
26 -{{{@article{Baldy2024AutoDCM,
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,
27 27   title={Dynamic Causal Modeling in Probabilistic Programming Languages},
28 28   author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam},
29 29   journal={bioRxiv},