58 citations · 131 across the 5 of their papers we have counts for
5 papers
Detecting and diagnosing prior and likelihood sensitivity with power-scaling
Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner +1
Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationall…
Group Heterogeneity Assessment for Multilevel Models
Topi Paananen, Alejandro Catalina, Paul-Christian Bürkner +1
Many data sets contain an inherent multilevel structure, for example, because of repeated measurements of the same observational units. Taking this structure into account is critic…
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…
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…
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…