Last modified by mhashemi on 2025/05/09 17:29
From version 13.1
edited by mhashemi
on 2025/05/09 16:54
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To version 11.1
edited by mhashemi
on 2024/12/03 16:24
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27 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 28  
29 29  
30 -Github: [[https:~~/~~/github.com/ins-amu/DCM_PPLs>>https://github.com/ins-amu/DCM_PPLs]]
31 -
32 -
33 33  Notebooks:
34 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]]
35 35  \\Tutorial:
... ... @@ -36,7 +36,7 @@
36 36  
37 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 38  
39 -{{{@article{AutoDCM,
36 +{{{@article{Baldy2024AutoDCM,
40 40   title={Dynamic Causal Modeling in Probabilistic Programming Languages},
41 41   author={Baldy, Nina and Woodman, Marmaduke and Jirsa, Viktor and Hashemi, Meysam},
42 42   journal={bioRxiv},