5 papers
Graph-Based Correlation Matrix Generation: A Convex Optimization Approach
Ali Fakhar, K{é}vin Polisano, Ir{è}ne Gannaz +1
This work addresses the generation of theoretical correlation matrices with prescribed sparsity patterns associated to graph structures. We propose a novel convex optimization fram…
Credible rectangles for high-dimensional posterior comparison
Alice Chevaux, Julyan Arbel, Guillaume Kon Kam King +1
We propose a Bayesian framework for uncertainty quantification and comparison in brain connectivity graph analysis. Standard graph-based approaches typically rely on point estimate…
Prior elicitation for Bayesian estimation of single-subject connectivity networks
Yiye Jiang, Alice Chevaux, Wendy Meiring +4
Inference of brain functional connectivity networks from resting-state fMRI data is a key focus in neuroimaging. This paper introduces new Bayesian approaches for inferring a funct…
Benchmarking Brain Connectivity Graph Inference: A Novel Validation Approach
Alice Chevaux, Ali Fahkar, Kévin Polisano +2
Inferring a binary connectivity graph from resting-state fMRI data for a single subject requires making several methodological choices and assumptions that can significantly affect…
Generating Correlation Matrices with Graph Structures Using Convex Optimization
Ali Fakhar, Kévin Polisano, Irène Gannaz +1
This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex…