Last modified by mhashemi on 2025/05/09 17:29
From version 10.2
edited by mhashemi
on 2024/12/03 16:24
Change comment: There is no comment for this version
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 += My Collab's Extended Title =
6 +
7 +My collab's subtitle
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9 +)))
7 7  
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15 += What can I find here? =
12 12  
13 -This tool was developed at INS in Marseille.
14 -Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
17 +* Notice how the table of contents on the right
18 +* is automatically updated
19 +* to hold this page's headers
15 15  
21 += Who has access? =
16 16  
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):
23 +Describe the audience of this collab.
24 +)))
18 18  
19 -Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]]
20 20  
21 -Blackjax: [[https:~~/~~/blackjax-devs.github.io/blackjax/>>url:https://blackjax-devs.github.io/blackjax/]]
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28 +(((
29 +{{box title="**Contents**"}}
30 +{{toc/}}
31 +{{/box}}
22 22  
23 -PyMC: [[https:~~/~~/www.pymc.io/welcome.html>>url:https://www.pymc.io/welcome.html]]
24 -
25 -Stan: [[https:~~/~~/mc-stan.org/>>url:https://mc-stan.org/]]
26 -
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.
28 -
29 -
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:
33 -
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]]
35 -
36 -{{{@article{Baldy2024AutoDCM,
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 -}}}
45 -
46 46  
47 47  )))
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