Last modified by mhashemi on 2024/12/03 18:27
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13 This tool was developed at INS in Marseille.
14 Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
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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):
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19 Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]]
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21 Blackjax: [[https:~~/~~/blackjax-devs.github.io/blackjax/>>url:https://blackjax-devs.github.io/blackjax/]]
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23 PyMC: [[https:~~/~~/www.pymc.io/welcome.html>>url:https://www.pymc.io/welcome.html]]
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25 Stan: [[https:~~/~~/mc-stan.org/>>url:https://mc-stan.org/]]
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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.
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30 Notebooks:
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:
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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]]
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36 {{{@article{AutoDCM,
37 title={Dynamic Causal Modeling in Probabilistic Programming Languages},
38 author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam},
39 journal={bioRxiv},
40 pages={2024--11},
41 year={2024},
42 publisher={Cold Spring Harbor Laboratory}
43 }
44 }}}
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