5.7k citations · 12k across the 78 of their papers we have counts for
10 papers · 1 filter
Prior knowledge elicitation: The past, present, and future
Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli +9
Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. I…
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…
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…
Pathfinder: Parallel quasi-Newton variational inference
Lu Zhang, Bob Carpenter, Andrew Gelman +1
We propose Pathfinder, a variational method for approximately sampling from differentiable log densities. Starting from a random initialization, Pathfinder locates normal approxima…
Detecting and diagnosing prior and likelihood sensitivity with power-scaling
Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner +1
Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationall…
The piranha problem: Large effects swimming in a small pond
Christopher Tosh, Philip Greengard, Ben Goodrich +3
In some scientific fields, it is common to have certain variables of interest that are of particular importance and for which there are many studies indicating a relationship with…
Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparison
Teemu Säilynoja, Paul-Christian Bürkner, Aki Vehtari
Assessing goodness of fit to a given distribution plays an important role in computational statistics. The Probability integral transformation (PIT) can be used to convert the ques…
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 -…
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…
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…