5 papers
From Mice to Trains: Amortized Bayesian Inference on Graph Data
Svenja Jedhoff, Elizaveta Semenova, Aura Raulo +2
Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data r…
Simulation-based validation of Bayes factor computation
Martin Modrák, Sebastian Stroppel, Paul-Christian Bürkner
We propose and evaluate two methods that validate the computation of Bayes factors: one based on an improved variant of simulation-based calibration checking (SBC) and one based on…
Unsupervised Continual Learning for Amortized Bayesian Inference
Aayush Mishra, Šimon Kucharský, Paul-Christian Bürkner
Amortized Bayesian Inference (ABI) enables efficient posterior estimation using generative neural networks trained on simulated data, but often suffers from performance degradation…
Hilbert space methods for approximating multi-output latent variable Gaussian processes
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approxim…
Posterior SBC: Simulation-Based Calibration Checking Conditional on Data
Teemu Säilynoja, Marvin Schmitt, Paul-Christian Bürkner +1
Simulation-based calibration checking (SBC) refers to the validation of an inference algorithm and model implementation through repeated inference on data simulated from a generati…