5.7k citations · 12k across the 78 of their papers we have counts for
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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…
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…
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…
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…
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael R. Andersen +2
Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intrac…
Implicitly Adaptive Importance Sampling
Topi Paananen, Juho Piironen, Paul-Christian Bürkner +1
Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability dis…
Selecting the Metric in Hamiltonian Monte Carlo
Ben Bales, Arya Pourzanjani, Aki Vehtari +1
We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the…
Rank-normalization, folding, and localization: An improved for assessing convergence of MCMC
Aki Vehtari, Andrew Gelman, Daniel Simpson +2
Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this pape…
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Aki Vehtari, Andrew Gelman, Jonah Gabry
Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bay…
Pareto Smoothed Importance Sampling
Aki Vehtari, Daniel Simpson, Andrew Gelman +2
Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the…
Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models
Aki Vehtari, Tommi Mononen, Ville Tolvanen +2
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integ…
Laplace approximation for logistic Gaussian process density estimation and regression
Jaakko Riihimäki, Aki Vehtari
Logistic Gaussian process (LGP) priors provide a flexible alternative for modelling unknown densities. The smoothness properties of the density estimates can be controlled through…