activity
20122021
most citedSparsity information and regularization in the horseshoe and other shrinkage priors

484 citations · 849 across the 12 of their papers we have counts for

collaborators

30 papers

stat.ME20211 cited

On Reparameterization Invariant Bayesian Point Estimates and Credible Regions

Aki Vehtari

This paper considers reparameterization invariant Bayesian point estimates and credible regions of model parameters for scientific inference and communication. The effect of intrin…

stat.CO20216 cited

Latent space projection predictive inference

Alejandro Catalina, Paul Bürkner, Aki Vehtari

Given a reference model that includes all the available variables, projection predictive inference replaces its posterior with a constrained projection including only a subset of a…

cs.LG2021

Challenges and Opportunities in High-dimensional Variational Inference

Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe +3

Current black-box variational inference (BBVI) methods require the user to make numerous design choices -- such as the selection of variational objective and approximating family -…

cs.LG20212 cited

Good practices for Bayesian Optimization of high dimensional structured spaces

Eero Siivola, Javier Gonzalez, Andrei Paleyes +1

The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervi…

stat.ME2021

Bayesian hierarchical stacking: Some models are (somewhere) useful

Yuling Yao, Gregor Pirš, Aki Vehtari +1

Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predict…

stat.ME2020

What are the most important statistical ideas of the past 50 years?

Andrew Gelman, Aki Vehtari

We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, over…