64 citations · 130 across the 26 of their papers we have counts for
18 papers · 1 filter
Approximating Bayesian leave-one-group-out cross-validation
Anna Elisabeth Riha, Svenja Jedhoff, Paul-Christian Bürkner +1
When data are grouped, hierarchical or multilevel models are commonly used to account for group-level variation with group-specific parameters. Leave-one-group-out cross-validation…
To select or not to select: predictively consistent priors instead of model selection
Anna Elisabeth Riha, Leevi Lindgren, David Kohns +2
Bayesian modelling workflows often consider multiple candidate models of varying complexity. Model selection is commonly used to navigate potential trade-offs between model complex…
LOO-PIT predictive model checking
Herman Tesso, Aki Vehtari
We consider predictive checking for Bayesian model assessment using leave-one-out probability integral transform (LOO-PIT). LOO-PIT values are conditional cumulative predictive pro…
Predictive Assessment and Comparison of Bayesian Survival Models for Cancer Recurrence
Saku Suorsa, Aki Vehtari
Complex data features, such as unmodelled censored event times and variables with time-dependent effects, are common in cancer recurrence studies and pose challenges for Bayesian s…
Efficient scenario analysis in real-time Bayesian election forecasting via sequential meta-posterior sampling
Geonhee Han, Andrew Gelman, Aki Vehtari
Bayesian aggregation lets election forecasters combine diverse sources of information, such as state polls and economic and political indicators: as in our collaboration with The E…
Bridge Sampling Diagnostics
Giorgio Micaletto, Aki Vehtari
In Bayesian statistics, the marginal likelihood is used for model selection and averaging, yet it is often challenging to compute accurately for complex models. Approaches such as…
R2 priors for Grouped Variance Decomposition in High-dimensional Regression
Javier Enrique Aguilar, David Kohns, Aki Vehtari +1
We introduce the Group-R2 decomposition prior, a hierarchical shrinkage prior that extends R2-based priors to structured regression settings with known groups of predictors. By dec…
Joint leave-group-out cross-validation in Bayesian spatial models
Alex Cooper, Aki Vehtari, Catherine Forbes
Cross-validation (CV) is a widely-used method of predictive assessment based on repeated model fits to different subsets of the available data. CV is applicable in a wide range of…
Posterior SBC: Simulation-Based Calibration Checking Conditional on Data
Teemu Säilynoja, Marvin Schmitt, Paul-Christian Bürkner +1
Simulation-based calibration checking (SBC) refers to the validation of an inference algorithm and model implementation through repeated inference on data simulated from a generati…
Recommendations for visual predictive checks in Bayesian workflow
Teemu Säilynoja, Andrew R. Johnson, Osvaldo A. Martin +1
A key step in the Bayesian workflow for model building is the graphical assessment of model predictions, whether these are drawn from the prior or posterior predictive distribution…
A note on Bayesian R-squared for generalized additive mixed models
Abdollah Jalilian, Aki Vehtari, Luigi Sedda
We present a novel Bayesian framework to decompose the posterior predictive variance in a fitted Generalized Additive Mixed Model (GAMM) into explained and unexplained components.…
Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis
Anna Elisabeth Riha, Nikolas Siccha, Antti Oulasvirta +1
When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate m…
Model validation for aggregate inferences in out-of-sample prediction
Lauren Kennedy, Aki Vehtari, Andrew Gelman
Generalization to new samples is a fundamental rationale for statistical modeling. For this purpose, model validation is particularly important, but recent work in survey inference…
Bayesian cross-validation by parallel Markov Chain Monte Carlo
Alex Cooper, Aki Vehtari, Catherine Forbes +2
Brute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or `brute force…
Projection predictive variable selection for discrete response families with finite support
Frank Weber, Änne Glass, Aki Vehtari
The projection predictive variable selection is a decision-theoretically justified Bayesian variable selection approach achieving an outstanding trade-off between predictive perfor…
Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity
Martin Modrák, Angie H. Moon, Shinyoung Kim +5
Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations. In this paper, we introduce a…
Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains
Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov +3
Recent developments in parallel Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains almost as quickly as a single chain, using hardware accelerators such…
Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison
Tuomas Sivula, Måns Magnusson, Asael Alonzo Matamoros +1
It is useful to estimate the expected predictive performance of models planned to be used for prediction. We focus on leave-one-out cross-validation (LOO-CV), which has become a po…