
Fast methods for posterior inference of twogroup normalnormal models
We describe a class of algorithms for evaluating posterior moments of ce...
read it

On Reparameterization Invariant Bayesian Point Estimates and Credible Regions
This paper considers reparameterization invariant Bayesian point estimat...
read it

Latent space projection predictive inference
Given a reference model that includes all the available variables, proje...
read it

Pathfinder: Parallel quasiNewton variational inference
We introduce Pathfinder, a variational method for approximately sampling...
read it

Detecting and diagnosing prior and likelihood sensitivity with powerscaling
Determining the sensitivity of the posterior to perturbations of the pri...
read it

The piranha problem: Large effects swimming in a small pond
In some scientific fields, it is common to have certain variables of int...
read it

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...
read it

Challenges and Opportunities in Highdimensional Variational Inference
We explore the limitations of and best practices for using blackbox var...
read it

Bayesian hierarchical stacking
Stacking is a widely used model averaging technique that yields asymptot...
read it

Good practices for Bayesian Optimization of high dimensional structured spaces
The increasing availability of structured but high dimensional data has ...
read it

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...
read it

A Fast Linear Regression via SVD and Marginalization
We describe a numerical scheme for evaluating the posterior moments of B...
read it

Bayesian Workflow
The Bayesian approach to data analysis provides a powerful way to handle...
read it

Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Projection predictive inference is a decision theoretic Bayesian approac...
read it

Robust, Accurate Stochastic Optimization for Variational Inference
We consider the problem of fitting variational posterior approximations ...
read it

Adaptive Path Sampling in Metastable Posterior Distributions
The normalizing constant plays an important role in Bayesian computation...
read it

Unbiased estimator for the variance of the leaveoneout crossvalidation estimator for a Bayesian normal model with fixed variance
When evaluating and comparing models using leaveoneout crossvalidatio...
read it

Uncertainty in Bayesian LeaveOneOut CrossValidation Based Model Comparison
Leaveoneout crossvalidation (LOOCV) is a popular method for comparin...
read it

Stacking for Nonmixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
When working with multimodal Bayesian posterior distributions, Markov ch...
read it

Group Heterogeneity Assessment for Multilevel Models
Many data sets contain an inherent multilevel structure, for example, be...
read it

Using reference models in variable selection
Variable selection, or more generally, model reduction is an important a...
read it

Hamiltonian Monte Carlo using an adjointdifferentiated Laplace approximation
Gaussian latent variable models are a key class of Bayesian hierarchical...
read it

Hamiltonian Monte Carlo using an embedded Laplace approximation
Latent Gaussian models are a popular class of hierarchical models with a...
read it

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful nonparametric probabilistic models for ...
read it

Preferential Batch Bayesian Optimization
Most research in Bayesian optimization (BO) has focused on direct feedba...
read it

When are Bayesian model probabilities overconfident?
Bayesian model comparison is often based on the posterior distribution o...
read it

LeaveOneOut CrossValidation for Bayesian Model Comparison in Large Data
Recently, new methods for model assessment, based on subsampling and pos...
read it

An interpretable probabilistic machine learning method for heterogeneous longitudinal studies
Identifying risk factors from longitudinal data requires statistical too...
read it

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...
read it

Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach
A salient approach to interpretable machine learning is to restrict mode...
read it

Ranking variables and interactions using predictive uncertainty measures
For complex nonlinear supervised learning models, assessing the relevanc...
read it

Batch simulations and uncertainty quantification in Gaussian process surrogatebased approximate Bayesian computation
Surrogate models such as Gaussian processes (GP) have been proposed to a...
read it

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
Surrogate models such as Gaussian processes (GP) have been proposed to a...
read it

Pushing the Limits of Importance Sampling through Iterative Moment Matching
The accuracy of an integral approximation via Monte Carlo sampling depen...
read it

Selecting the Metric in Hamiltonian Monte Carlo
We present a selection criterion for the Euclidean metric adapted during...
read it

Parallel Gaussian process surrogate method to accelerate likelihoodfree inference
We consider Bayesian inference when only a limited number of noisy logl...
read it

Bayesian leaveoneout crossvalidation for large data
Model inference, such as model comparison, model checking, and model sel...
read it

Active Learning for DecisionMaking from Imbalanced Observational Data
Machine learning can help personalized decision support by learning mode...
read it

Ranknormalization, folding, and localization: An improved R for assessing convergence of MCMC
Markov chain Monte Carlo is a key computational tool in Bayesian statist...
read it

Approximate leavefutureout crossvalidation for Bayesian time series models
One of the common goals of time series analysis is to use the observed s...
read it

Approximate leavefutureout crossvalidation for time series models
One of the common goals of time series analysis is to use the observed s...
read it

Leaveoneout crossvalidation for nonfactorizable normal models
Crossvalidation can be used to measure a model's predictive accuracy fo...
read it

Limitations of "Limitations of Bayesian leaveoneout crossvalidation for model selection"
This article is an invited discussion of the article by Gronau and Wagen...
read it

Projective Inference in Highdimensional Problems: Prediction and Feature Selection
This paper discusses predictive inference and feature selection for gene...
read it

Validating Bayesian Inference Algorithms with SimulationBased Calibration
Verifying the correctness of Bayesian computation is challenging. This i...
read it

Yes, but Did It Work?: Evaluating Variational Inference
While it's always possible to compute a variational approximation to a p...
read it

Bayesian Estimation of Gaussian Graphical Models with Projection Predictive Selection
Gaussian graphical models are used for determining conditional relations...
read it

Model selection for Gaussian processes utilizing sensitivity of posterior predictive distribution
We propose two novel methods for simplifying Gaussian process (GP) model...
read it

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
In humanintheloop machine learning, the user provides information bey...
read it

ELFI: Engine for Likelihood Free Inference
The Engine for LikelihoodFree Inference (ELFI) is a Python software lib...
read it