2 citations · 2 across the 2 of their papers we have counts for
4 papers
Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision
Hans Olischläger, Svenja Jedhoff, Šimon Kucharský +3
Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intractable statistical models and of…
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.…
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…
Amortized Bayesian Mixture Models
Šimon Kucharský, Paul Christian Bürkner
Finite mixtures are a broad class of models useful in scenarios where observed data is generated by multiple distinct processes but without explicit information about the responsib…