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Jan Boelts

2 accepted papers

2022

GATSBI: Generative Adversarial Training for Simulation-Based Inference

ICLR 2022poster

Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not require explicit likelihoods. We study the relationship between SBI and GANs, and i…

2021

Benchmarking Simulation-Based Inference

AISTATS 2021poster

Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a public benchmark with appropriate performance metrics for such ’likelihood-free’ algorithms has been lacking. This has m…