Last modified by mhashemi on 2025/11/06 14:04
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13 This open-source tool, called DCM_PPLs, was developed at INS in Marseille.
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16 Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
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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):
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21 Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]]
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23 Blackjax: [[https:~~/~~/blackjax-devs.github.io/blackjax/>>url:https://blackjax-devs.github.io/blackjax/]]
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25 PyMC: [[https:~~/~~/www.pymc.io/welcome.html>>url:https://www.pymc.io/welcome.html]]
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27 Stan: [[https:~~/~~/mc-stan.org/>>url:https://mc-stan.org/]]
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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.
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32 Github: [[https:~~/~~/github.com/ins-amu/DCM_PPLs>>https://github.com/ins-amu/DCM_PPLs]]
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35 Notebooks:
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:
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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]]
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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},
51 }
52 }}}
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