Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -9,4 +9,39 @@
9	9	(((
10	10
11	11	)))
	12	+
	13	+This tool was developed at INS in Marseille.
	14	+Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi
	15	+
	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):
	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/]]
	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	+
12	12	)))