b'Aki Vehtari'

research

∙ 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...

0 Yann McLatchie, et al. ∙

research

∙ 06/27/2023

Robust and efficient projection predictive inference

The concepts of Bayesian prediction, model comparison, and model selecti...

0 Yann McLatchie, et al. ∙

research

∙ 01/19/2023

Cross-validatory model selection for Bayesian autoregressions with exogenous regressors

Bayesian cross-validation (CV) is a popular method for predictive model ...

0 Alex cooper, et al. ∙

research

∙ 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...

0 Frank Weber, et al. ∙

research

∙ 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...

0 Martin Modrák, et al. ∙

research

∙ 08/31/2022

Bayesian order identification of ARMA models with projection predictive inference

Auto-regressive moving-average (ARMA) models are ubiquitous forecasting ...

0 Yann McLatchie, et al. ∙

research

∙ 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...

0 Juho Timonen, et al. ∙

research

∙ 03/29/2022

Robust, Automated, and Accurate Black-box Variational Inference

Black-box variational inference (BBVI) now sees widespread use in machin...

0 Manushi Welandawe, et al. ∙

research

∙ 12/01/2021

Prior knowledge elicitation: The past, present, and future

Specification of the prior distribution for a Bayesian model is a centra...

0 Petrus Mikkola, et al. ∙

research

∙ 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...

0 Philip Greengard, et al. ∙

research

∙ 09/22/2021

On Reparameterization Invariant Bayesian Point Estimates and Credible Regions

This paper considers reparameterization invariant Bayesian point estimat...

0 Aki Vehtari, et al. ∙

research

∙ 09/10/2021

Latent space projection predictive inference

Given a reference model that includes all the available variables, proje...

0 Alejandro Catalina, et al. ∙

research

∙ 08/09/2021

Pathfinder: Parallel quasi-Newton variational inference

We introduce Pathfinder, a variational method for approximately sampling...

9 Lu Zhang, et al. ∙

research

∙ 07/29/2021

Detecting and diagnosing prior and likelihood sensitivity with power-scaling

Determining the sensitivity of the posterior to perturbations of the pri...

0 Noa Kallioinen, et al. ∙

research

∙ 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...

0 Christopher Tosh, et al. ∙

research

∙ 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...

0 Teemu Säilynoja, et al. ∙

research

∙ 03/01/2021

Challenges and Opportunities in High-dimensional Variational Inference

We explore the limitations of and best practices for using black-box var...

17 Akash Kumar Dhaka, et al. ∙

research

∙ 01/22/2021

Bayesian hierarchical stacking

Stacking is a widely used model averaging technique that yields asymptot...

7 Yuling Yao, et al. ∙

research

∙ 12/31/2020

Good practices for Bayesian Optimization of high dimensional structured spaces

The increasing availability of structured but high dimensional data has ...

0 Eero Siivola, et al. ∙

research

∙ 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...

0 Andrew Gelman, et al. ∙

research

∙ 11/09/2020

A Fast Linear Regression via SVD and Marginalization

We describe a numerical scheme for evaluating the posterior moments of B...

0 Philip Greengard, et al. ∙

research

∙ 11/03/2020

Bayesian Workflow

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

0 Andrew Gelman, et al. ∙

research

∙ 10/14/2020

Projection Predictive Inference for Generalized Linear and Additive Multilevel Models

Projection predictive inference is a decision theoretic Bayesian approac...

0 Alejandro Catalina, et al. ∙

research

∙ 09/01/2020

Robust, Accurate Stochastic Optimization for Variational Inference

We consider the problem of fitting variational posterior approximations ...

8 Akash Kumar Dhaka, et al. ∙

research

∙ 09/01/2020

Adaptive Path Sampling in Metastable Posterior Distributions

The normalizing constant plays an important role in Bayesian computation...

0 Yuling Yao, et al. ∙

research

∙ 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...

0 Tuomas Sivula, et al. ∙

research

∙ 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...

0 Tuomas Sivula, et al. ∙

research

∙ 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...

11 Yuling Yao, et al. ∙

research

∙ 05/06/2020

Group Heterogeneity Assessment for Multilevel Models

Many data sets contain an inherent multilevel structure, for example, be...

1 Topi Paananen, et al. ∙

research

∙ 04/27/2020

Using reference models in variable selection

Variable selection, or more generally, model reduction is an important a...

0 Federico Pavone, et al. ∙

research

∙ 04/27/2020

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation

Gaussian latent variable models are a key class of Bayesian hierarchical...

0 Charles C. Margossian, et al. ∙

research

∙ 04/27/2020

Hamiltonian Monte Carlo using an embedded Laplace approximation

Latent Gaussian models are a popular class of hierarchical models with a...

0 Charles C. Margossian, et al. ∙

research

∙ 04/23/2020

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming

Gaussian processes are powerful non-parametric probabilistic models for ...

0 Gabriel Riutort-Mayol, et al. ∙

research

∙ 03/25/2020

Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on direct feedba...

14 Eero Siivola, et al. ∙

research

∙ 03/09/2020

When are Bayesian model probabilities overconfident?

Bayesian model comparison is often based on the posterior distribution o...

0 Oscar Oelrich, et al. ∙

research

∙ 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...

0 Måns Magnusson, et al. ∙

research

∙ 12/07/2019

An interpretable probabilistic machine learning method for heterogeneous longitudinal studies

Identifying risk factors from longitudinal data requires statistical too...

24 Juho Timonen, et al. ∙

research

∙ 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...

0 Gabriel Riutort-Mayol, et al. ∙

research

∙ 10/21/2019

Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach

A salient approach to interpretable machine learning is to restrict mode...

52 Homayun Afrabandpey, et al. ∙

research

∙ 10/17/2019

Ranking variables and interactions using predictive uncertainty measures

For complex nonlinear supervised learning models, assessing the relevanc...

29 Topi Paananen, et al. ∙

research

∙ 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...

4 Marko Järvenpää, et al. ∙

research

∙ 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...

0 Marko Järvenpää, et al. ∙

research

∙ 06/20/2019

Pushing the Limits of Importance Sampling through Iterative Moment Matching

The accuracy of an integral approximation via Monte Carlo sampling depen...

0 Topi Paananen, et al. ∙

research

∙ 05/28/2019

Selecting the Metric in Hamiltonian Monte Carlo

We present a selection criterion for the Euclidean metric adapted during...

0 Ben Bales, et al. ∙

research

∙ 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...

0 Marko Järvenpää, et al. ∙

research

∙ 04/24/2019

Bayesian leave-one-out cross-validation for large data

Model inference, such as model comparison, model checking, and model sel...

20 Måns Magnusson, et al. ∙

research

∙ 04/10/2019

Active Learning for Decision-Making from Imbalanced Observational Data

Machine learning can help personalized decision support by learning mode...

14 Iiris Sundin, et al. ∙

research

∙ 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...

0 Aki Vehtari, et al. ∙

research

∙ 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...

0 Paul-Christian Bürkner, et al. ∙

research

∙ 02/17/2019

Approximate leave-future-out cross-validation for time series models

One of the common goals of time series analysis is to use the observed s...

0 Paul-Christian Bürkner, et al. ∙

Aki Vehtari

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