We’re happy to share the second accepted workshop at StanCon 2026 in Uppsala, Sweden!
Bayesian Inference for Sparsity-Promoting and Edge-Preserving Priors in Probabilistic Programming
This workshop will highlight emerging methods for computationally efficient Bayesian inference in inverse problems and related high-dimensional models with sparsity-promoting and edge-preserving priors. Such priors often induce heavy-tailed, multimodal posteriors that challenge standard sampling and optimization, motivating new scalable strategies including hierarchical and mixture prior constructions, diffusion- and transport-based sampling, and advanced approaches to uncertainty quantification. A central aim is to connect these methodological developments to practical implementation in modern probabilistic programming frameworks—particularly Turing/Julia and, more broadly, Stan—emphasizing algorithmic advances that enable efficient inference at scale. The program will consist of curated invited talks followed by discussion sessions designed to foster exchange between method developers and users, identify key opportunities for probabilistic programming practitioners, and catalyze cross-community collaboration spanning inverse problems, Bayesian computation, numerical analysis, data assimilation, and scientific machine learning.
Link: https://www.janglaubitz.com/stancon2026
Organizers: Jan Glaubitz (Linköping University), Yiqiu Dong (Technical University of Denmark), Lassi Roininen (LUT University)
— The Local Organising Committee
@mans_magnusson @hamis @avehtari
We thank our sponsors, The Beijer Foundation, eSSENCE, The Swedish Excellence Centre for Social Science and Carl Tryggers Foundation. More information about the sponsors can be found here: Sponsors – Stan Conference 2026