Wiki source code of Dynamic Causal Modeling in Probabilistic Programming Languages
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13.2 | 13 | This tool called DCM_PPLs, was developed at INS in Marseille. |
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10.1 | 16 | Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi |
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13.2 | 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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10.2 | 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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13.1 | 32 | Github: [[https:~~/~~/github.com/ins-amu/DCM_PPLs>>https://github.com/ins-amu/DCM_PPLs]] |
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10.1 | 35 | Notebooks: |
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9.1 | 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]] |
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10.1 | 37 | \\Tutorial: |
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10.1 | 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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12.1 | 42 | {{{@article{AutoDCM, |
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9.1 | 43 | title={Dynamic Causal Modeling in Probabilistic Programming Languages}, |
| 44 | author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam}, | ||
| 45 | journal={bioRxiv}, | ||
| 46 | pages={2024--11}, | ||
| 47 | year={2024}, | ||
| 48 | publisher={Cold Spring Harbor Laboratory} | ||
| 49 | } | ||
| 50 | }}} | ||
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1.1 | 53 | ))) |