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	1	+This tool was developed at INS in Marseille.
	2	+Authors: M Hashemi, A Ziaeemehr, MM Woodman, J Fousek, S Petkoski, VK Jirsa
	3	+
1	1	Virtual Brain Models imply latent nonlinear state space models
2	2	driven by noise and network input, necessitating advanced probabilistic
3	3	machine learning techniques for widely applicable Bayesian estimation.
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6	6	functional features allows for accurate estimation of generative parameters in brain disorders.
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8	8
	12	+Code: [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/SBI-VBM>>https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/SBI-VBM]]
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10		-
11		-Code: [[https:~~/~~/github.com/ins-amu/SBI-VBMs>>https://github.com/ins-amu/SBI-VBMs]]
12		-
13	13	(% style="text-align: justify;" %)
14	14	Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]
	16	+
	17	+{{{
	18	+@article{SBI-VBM,
	19	+ title={Simulation-based inference on virtual brain models of disorders},
	20	+ author={Hashemi, Meysam and Ziaeemehr, Abolfazl and Woodman, Marmaduke M and Fousek, Jan and Petkoski, Spase and Jirsa, Viktor K},
	21	+ journal={Machine Learning: Science and Technology},
	22	+ volume={5},
	23	+ number={3},
	24	+ pages={035019},
	25	+ year={2024},
	26	+ publisher={IOP Publishing}
	27	+}
	28	+}}}