16 papers
Consistency of variational approximations under bounded Kullback--Leibler divergence
Hien Duy Nguyen, Jacob Westerhout, Thomas Guilmeau +1
Variational methods are widely used to approximate posterior distributions in Bayesian inference when exact computation is infeasible. We study when such approximations inherit pos…
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…
: Transformer-based inference from interaction maps
Eloïse Touron, Pedro L. C. Rodrigues, Julyan Arbel +2
Inference from interaction maps, such as centromere identification from genome-wide chromosome conformation capture techniques -- notably Hi-C -- can be formulated as a generic inv…
Theoretical guidelines for annealed Langevin dynamics in compositional simulation-based inference
Camille Touron, Gabriel V. Cardoso, Julyan Arbel +1
Compositional score-based approaches to simulation-based inference (SBI) approximate the posterior over a shared parameter given independent observations by aggregating individ…
Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation
Rafael Mouallem Rosa, Julyan Arbel, Hien Duy Nguyen
We introduce a general framework that extends Bayesian inference by allowing the researcher to explicitly encode confidence in each source of uncertainty within the model. This mec…
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…