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09/07/2023
Efficient estimation and correction of selection-induced bias with order statistics
Model selection aims to identify a sufficiently well performing model th...
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06/27/2023
Robust and efficient projection predictive inference
The concepts of Bayesian prediction, model comparison, and model selecti...
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01/19/2023
Cross-validatory model selection for Bayesian autoregressions with exogenous regressors
Bayesian cross-validation (CV) is a popular method for predictive model ...
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01/04/2023
Projection predictive variable selection for discrete response families with finite support
The approximate latent-space approach to the projective part of the proj...
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11/04/2022
Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity
Simulation-based calibration checking (SBC) is a practical method to val...
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08/31/2022
Bayesian order identification of ARMA models with projection predictive inference
Auto-regressive moving-average (ARMA) models are ubiquitous forecasting ...
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05/18/2022
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models
Statistical models can involve implicitly defined quantities, such as so...
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03/29/2022
Robust, Automated, and Accurate Black-box Variational Inference
Black-box variational inference (BBVI) now sees widespread use in machin...
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12/01/2021
Prior knowledge elicitation: The past, present, and future
Specification of the prior distribution for a Bayesian model is a centra...
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10/06/2021
Fast methods for posterior inference of two-group normal-normal models
We describe a class of algorithms for evaluating posterior moments of ce...
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09/22/2021
On Reparameterization Invariant Bayesian Point Estimates and Credible Regions
This paper considers reparameterization invariant Bayesian point estimat...
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09/10/2021
Latent space projection predictive inference
Given a reference model that includes all the available variables, proje...
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08/09/2021
Pathfinder: Parallel quasi-Newton variational inference
We introduce Pathfinder, a variational method for approximately sampling...
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07/29/2021
Detecting and diagnosing prior and likelihood sensitivity with power-scaling
Determining the sensitivity of the posterior to perturbations of the pri...
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05/27/2021
The piranha problem: Large effects swimming in a small pond
In some scientific fields, it is common to have certain variables of int...
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03/18/2021
Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparison
Assessing goodness of fit to a given distribution plays an important rol...
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03/01/2021
Challenges and Opportunities in High-dimensional Variational Inference
We explore the limitations of and best practices for using black-box var...
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01/22/2021
Bayesian hierarchical stacking
Stacking is a widely used model averaging technique that yields asymptot...
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12/31/2020
Good practices for Bayesian Optimization of high dimensional structured spaces
The increasing availability of structured but high dimensional data has ...
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11/30/2020
What are the most important statistical ideas of the past 50 years?
We argue that the most important statistical ideas of the past half cent...
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11/09/2020
A Fast Linear Regression via SVD and Marginalization
We describe a numerical scheme for evaluating the posterior moments of B...
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11/03/2020
Bayesian Workflow
The Bayesian approach to data analysis provides a powerful way to handle...
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10/14/2020
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Projection predictive inference is a decision theoretic Bayesian approac...
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09/01/2020
Robust, Accurate Stochastic Optimization for Variational Inference
We consider the problem of fitting variational posterior approximations ...
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09/01/2020
Adaptive Path Sampling in Metastable Posterior Distributions
The normalizing constant plays an important role in Bayesian computation...
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08/25/2020
Unbiased estimator for the variance of the leave-one-out cross-validation estimator for a Bayesian normal model with fixed variance
When evaluating and comparing models using leave-one-out cross-validatio...
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08/24/2020
Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison
Leave-one-out cross-validation (LOO-CV) is a popular method for comparin...
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06/22/2020
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
When working with multimodal Bayesian posterior distributions, Markov ch...
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05/06/2020
Group Heterogeneity Assessment for Multilevel Models
Many data sets contain an inherent multilevel structure, for example, be...
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04/27/2020
Using reference models in variable selection
Variable selection, or more generally, model reduction is an important a...
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04/27/2020
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation
Gaussian latent variable models are a key class of Bayesian hierarchical...
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04/27/2020
Hamiltonian Monte Carlo using an embedded Laplace approximation
Latent Gaussian models are a popular class of hierarchical models with a...
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04/23/2020
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful non-parametric probabilistic models for ...
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03/25/2020
Preferential Batch Bayesian Optimization
Most research in Bayesian optimization (BO) has focused on direct feedba...
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03/09/2020
When are Bayesian model probabilities overconfident?
Bayesian model comparison is often based on the posterior distribution o...
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01/03/2020
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data
Recently, new methods for model assessment, based on subsampling and pos...
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12/07/2019
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies
Identifying risk factors from longitudinal data requires statistical too...
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11/07/2019
Gaussian process with derivative information for the analysis of the sunlight adverse effects on color of rock art paintings
Microfading Spectrometry (MFS) is a method for assessing light sensitivi...
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10/21/2019
Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach
A salient approach to interpretable machine learning is to restrict mode...
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10/17/2019
Ranking variables and interactions using predictive uncertainty measures
For complex nonlinear supervised learning models, assessing the relevanc...
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10/14/2019
Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation
Surrogate models such as Gaussian processes (GP) have been proposed to a...
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10/14/2019
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
Surrogate models such as Gaussian processes (GP) have been proposed to a...
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06/20/2019
Pushing the Limits of Importance Sampling through Iterative Moment Matching
The accuracy of an integral approximation via Monte Carlo sampling depen...
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05/28/2019
Selecting the Metric in Hamiltonian Monte Carlo
We present a selection criterion for the Euclidean metric adapted during...
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05/03/2019
Parallel Gaussian process surrogate method to accelerate likelihood-free inference
We consider Bayesian inference when only a limited number of noisy log-l...
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04/24/2019
Bayesian leave-one-out cross-validation for large data
Model inference, such as model comparison, model checking, and model sel...
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04/10/2019
Active Learning for Decision-Making from Imbalanced Observational Data
Machine learning can help personalized decision support by learning mode...
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03/19/2019
Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC
Markov chain Monte Carlo is a key computational tool in Bayesian statist...
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02/17/2019
Approximate leave-future-out cross-validation for Bayesian time series models
One of the common goals of time series analysis is to use the observed s...
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02/17/2019
