143 citations · 159 across the 16 of their papers we have counts for
34 papers
To select or not to select: predictively consistent priors instead of model selection
Anna Elisabeth Riha, Leevi Lindgren, David Kohns +2
Bayesian modelling workflows often consider multiple candidate models of varying complexity. Model selection is commonly used to navigate potential trade-offs between model complex…
Latent Variable Models for Distributional Features
Luna Fazio, Paul-Christian Bürkner
Analyzing the mean response of study subjects in psychological research is a standard, well-justified practice. However, theoretical arguments and empirical evidence also suggest t…
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
Lars Kühmichel, Jerry M. Huang, Valentin Pratz +11
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows.…
Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Šimon Kucharský, Aayush Mishra, Daniel Habermann +2
Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However,…
Latent variable estimation with composite Hilbert space Gaussian processes
Soham Mukherjee, Javier Enrique Aguilar, Marcello Zago +2
We develop a scalable class of models for latent variable estimation using composite Gaussian processes, with a focus on derivative Gaussian processes. We jointly model multiple da…
Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Šimon Kucharský, Aayush Mishra, Daniel Habermann +2
Amortized Bayesian model comparison (BMC) enables fast probabilistic ranking of models via simulation-based training of neural surrogates. However, the accuracy of neural surrogate…