5.7k citations · 12k across the 78 of their papers we have counts for
79 papers
Efficient estimation and correction of selection-induced bias with order statistics
Yann McLatchie, Aki Vehtari
Model selection aims to identify a sufficiently well performing model that is possibly simpler than the most complex model among a pool of candidates. However, the decision-making…
Advances in projection predictive inference
Yann McLatchie, Sölvi Rögnvaldsson, Frank Weber +1
The concepts of Bayesian prediction, model comparison, and model selection have developed significantly over the last decade. As a result, the Bayesian community has witnessed a ra…
Cross-validatory model selection for Bayesian autoregressions with exogenous regressors
Alex Cooper, Dan Simpson, Lauren Kennedy +2
Bayesian cross-validation (CV) is a popular method for predictive model assessment that is simple to implement and broadly applicable. A wide range of CV schemes is available for t…
Bayesian order identification of ARMA models with projection predictive inference
Yann McLatchie, Asael Alonzo Matamoros, David Kohns +1
Auto-regressive moving-average (ARMA) models are ubiquitous forecasting tools. Parsimony in such models is highly valued for their interpretability and computational tractability,…
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models
Juho Timonen, Nikolas Siccha, Ben Bales +2
Statistical models can involve implicitly defined quantities, such as solutions to nonlinear ordinary differential equations (ODEs), that unavoidably need to be numerically approxi…
A Framework for Improving the Reliability of Black-box Variational Inference
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari +1
Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approxi…
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…
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…
A Fast Linear Regression via SVD and Marginalization
Philip Greengard, Andrew Gelman, Aki Vehtari
We describe a numerical scheme for evaluating the posterior moments of Bayesian linear regression models with partial pooling of the coefficients. The principal analytical tool of…
Bayesian Workflow
Andrew Gelman, Aki Vehtari, Daniel Simpson +7
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilis…
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Alejandro Catalina, Paul-Christian Bürkner, Aki Vehtari
Projection predictive inference is a decision theoretic Bayesian approach that decouples model estimation from decision making. Given a reference model previously built including a…
Robust, Accurate Stochastic Optimization for Variational Inference
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen +3
We consider the problem of fitting variational posterior approximations using stochastic optimization methods. The performance of these approximations depends on (1) how well the v…
Adaptive Path Sampling in Metastable Posterior Distributions
Yuling Yao, Collin Cademartori, Aki Vehtari +1
The normalizing constant plays an important role in Bayesian computation, and there is a large literature on methods for computing or approximating normalizing constants that canno…
Unbiased estimator for the variance of the leave-one-out cross-validation estimator for a Bayesian normal model with fixed variance
Tuomas Sivula, Måns Magnusson, Aki Vehtari
When evaluating and comparing models using leave-one-out cross-validation (LOO-CV), the uncertainty of the estimate is typically assessed using the variance of the sampling distrib…
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Yuling Yao, Aki Vehtari, Andrew Gelman
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-base…