5.7k citations · 12k across the 78 of their papers we have counts for
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Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
Topi Paananen, Juho Piironen, Michael Riis Andersen +1
Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a prox…
Iterative Supervised Principal Components
Juho Piironen, Aki Vehtari
In high-dimensional prediction problems, where the number of features may greatly exceed the number of training instances, fully Bayesian approach with a sparsifying prior is known…
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
Pedram Daee, Tomi Peltola, Aki Vehtari +1
In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facili…
Visualization in Bayesian workflow
Jonah Gabry, Daniel Simpson, Aki Vehtari +2
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian da…
ELFI: Engine for Likelihood-Free Inference
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö +7
Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components…
Sparsity information and regularization in the horseshoe and other shrinkage priors
Juho Piironen, Aki Vehtari
The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian estimation, but has previously suffered from two problems. First, there has been no systematic way…
Nudged elastic band calculations accelerated with Gaussian process regression
Olli-Pekka Koistinen, Freyja B. Dagbjartsdóttir, Vilhjálmur Ásgeirsson +2
Minimum energy paths for transitions such as atomic and/or spin rearrangements in thermalized systems are the transition paths of largest statistical weight. Such paths are frequen…
Using stacking to average Bayesian predictive distributions
Yuling Yao, Aki Vehtari, Daniel Simpson +1
The widely recommended procedure of Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit.…
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Eero Siivola, Aki Vehtari, Jarno Vanhatalo +2
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^…
Efficient acquisition rules for model-based approximate Bayesian computation
Marko Järvenpää, Michael U. Gutmann, Arijus Pleska +2
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms…
Minimum energy path calculations with Gaussian process regression
Olli-Pekka Koistinen, Emile Maras, Aki Vehtari +1
The calculation of minimum energy paths for transitions such as atomic and/or spin re-arrangements is an important task in many contexts and can often be used to determine the mech…