Wiki source code of Dynamic Causal Modeling in Probabilistic Programming Languages
Hide last authors
| author | version | line-number | content |
|---|---|---|---|
 |
1.1 | 1 | (% class="row" %) |
| 2 | ((( | ||
| 3 | (% class="col-xs-12 col-sm-8" %) | ||
| 4 | ((( | ||
| |
7.1 | 5 | |
| |
1.1 | 6 | ))) |
| 7 | |||
| 8 | (% class="col-xs-12 col-sm-4" %) | ||
| 9 | ((( | ||
| 10 | |||
| 11 | ))) | ||
| |
9.1 | 12 | |
| |
10.1 | 13 | This tool was developed at INS in Marseille. |
| 14 | Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi | ||
| 15 | |||
| 16 | |||
| |
9.1 | 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): |
| 18 | |||
| 19 | Numpyro: [[https:~~/~~/num.pyro.ai/en/stable/>>url:https://num.pyro.ai/en/stable/]] | ||
| 20 | |||
| 21 | Blackjax: [[https:~~/~~/blackjax-devs.github.io/blackjax/>>url:https://blackjax-devs.github.io/blackjax/]] | ||
| 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/]] | ||
| |
10.1 | 26 | |
| |
10.2 | 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. |
| |
10.1 | 28 | |
| |
10.2 | 29 | |
| |
13.1 | 30 | Github: [[https:~~/~~/github.com/ins-amu/DCM_PPLs>>https://github.com/ins-amu/DCM_PPLs]] |
| 31 | |||
| 32 | |||
| |
10.1 | 33 | Notebooks: |
| |
9.1 | 34 | \\[[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]] |
| |
10.1 | 35 | \\Tutorial: |
| |
9.1 | 36 | |
| |
10.1 | 37 | [[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]] |
| 38 | |||
| |
12.1 | 39 | {{{@article{AutoDCM, |
| |
9.1 | 40 | title={Dynamic Causal Modeling in Probabilistic Programming Languages}, |
| 41 | author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam}, | ||
| 42 | journal={bioRxiv}, | ||
| 43 | pages={2024--11}, | ||
| 44 | year={2024}, | ||
| 45 | publisher={Cold Spring Harbor Laboratory} | ||
| 46 | } | ||
| 47 | }}} | ||
| 48 | |||
| 49 | |||
| |
1.1 | 50 | ))) |