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20242026
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5 papers · 1 filter

stat.ME2026

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…

stat.ME2026

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…

stat.ME2026

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…

stat.ME2026

A variational framework for modal estimation

Tâm LeMinh, Julyan Arbel, Florence Forbes +1

We approach multivariate mode estimation through Gibbs distributions and introduce GERVE (Gibbs-measure Entropy-Regularised Variational Estimation), a likelihood-free framework tha…

stat.ME2026

Species Sensitivity Distribution revisited: a Bayesian nonparametric approach

Louise Alamichel, Julyan Arbel, Guillaume Kon Kam King +1

We present a novel approach to ecological risk assessment by recasting the Species Sensitivity Distribution (SSD) method within a Bayesian nonparametric (BNP) framework. Widely man…