Topic: Monte Carlo sampling

Virtual brain modeling is a data-driven approach that combines personalized anatomical information with mathematical models of brain activity to generate spatiotemporal patterns as observed in brain imaging signals. In this study our aim is to conduct inference and prediction on the local and global dynamics using virtual brain models, affected by disorders. Inference algorithms are necessary to efficiently estimate the unknown parameters (such as regional excitability parameters and global scaling factor on connectome), ideally incorporating measures of uncertainty. In this work, we provide f