5.7k citations · 12k across the 78 of their papers we have counts for
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lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data
Juho Timonen, Henrik Mannerström, Aki Vehtari +1
Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can…
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework
Homayun Afrabandpey, Tomi Peltola, Juho Piironen +2
A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure and…
Uncertainty-aware Sensitivity Analysis Using Rényi Divergences
Topi Paananen, Michael Riis Andersen, Aki Vehtari
For nonlinear supervised learning models, assessing the importance of predictor variables or their interactions is not straightforward because it can vary in the domain of the vari…
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
Marko Järvenpää, Aki Vehtari, Pekka Marttinen
The computational efficiency of approximate Bayesian computation (ABC) has been improved by using surrogate models such as Gaussian processes (GP). In one such promising framework…
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…
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
Marko Järvenpää, Michael Gutmann, Aki Vehtari +1
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical mode…
Bayesian leave-one-out cross-validation for large data
Måns Magnusson, Michael Riis Andersen, Johan Jonasson +1
Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approac…
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin, Peter Schulam, Eero Siivola +3
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based deci…
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…
Approximate leave-future-out cross-validation for Bayesian time series models
Paul-Christian Bürkner, Jonah Gabry, Aki Vehtari
One of the common goals of time series analysis is to use the observed series to inform predictions for future observations. In the absence of any actual new data to predict, cross…