Last modified by mhashemi on 2025/05/09 17:29
From version 14.2
edited by mhashemi
on 2025/05/09 17:20
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To version 5.1
edited by mhashemi
on 2024/11/27 17:33
Change comment: There is no comment for this version

Summary

Details

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2 2  (((
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5 -
5 += My Collab's Extended Title =
6 +
7 +My collab's subtitle
6 6  )))
9 +)))
7 7  
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15 += What can I find here? =
12 12  
13 -This open-source tool, called DCM_PPLs, was developed at INS in Marseille.
17 +* Notice how the table of contents on the right
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14 14  
21 += Who has access? =
15 15  
16 -Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
23 +Describe the audience of this collab.
24 +)))
17 17  
18 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):
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28 +(((
29 +{{box title="**Contents**"}}
30 +{{toc/}}
31 +{{/box}}
20 20  
21 -Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]]
22 -
23 -Blackjax: [[https:~~/~~/blackjax-devs.github.io/blackjax/>>url:https://blackjax-devs.github.io/blackjax/]]
24 -
25 -PyMC: [[https:~~/~~/www.pymc.io/welcome.html>>url:https://www.pymc.io/welcome.html]]
26 -
27 -Stan: [[https:~~/~~/mc-stan.org/>>url:https://mc-stan.org/]]
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:
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:
38 -
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,
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 -}}}
51 -
52 52  
53 53  )))
35 +)))
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