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Last modified by mhashemi on 2025/11/06 14:04
From version 9.1
edited by mhashemi
on 2024/11/27 17:47
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To version 18.1
edited by mhashemi
on 2025/11/06 14:02
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Title
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1 -Dynamic Causal Modeling in Probabilistic Programming Languages
1 +Dynamic Causal Modeling in Probabilistic Programming Languages (DCM_PPLs)
Content
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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 open-source 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/]]
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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,
27 - title={Dynamic Causal Modeling in Probabilistic Programming Languages},
28 - author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam},
29 - journal={bioRxiv},
30 - pages={2024--11},
31 - year={2024},
32 - publisher={Cold Spring Harbor Laboratory}
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{DCM_PPLs,
43 +author = {Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor K. and Hashemi, Meysam},
44 +title = {Dynamic causal modelling in probabilistic programming languages},
45 +journal = {Journal of The Royal Society Interface},
46 +volume = {22},
47 +number = {227},
48 +pages = {20240880},
49 +year = {2025},
50 +doi = {10.1098/rsif.2024.0880},
33 33  }
34 34  }}}
35 35