258 citations · 1k across the 30 of their papers we have counts for
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Efficient identification of informative features in simulation-based inference
Jonas Beck, Michael Deistler, Yves Bernaerts +2
Simulation-based Bayesian inference (SBI) can be used to estimate the parameters of complex mechanistic models given observed model outputs without requiring access to explicit lik…
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Jonas Wildberger, Maximilian Dax, Stephen R. Green +5
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accur…
Truncated proposals for scalable and hassle-free simulation-based inference
Michael Deistler, Pedro J Goncalves, Jakob H Macke
Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To i…
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
Maximilian Dax, Stephen R. Green, Jonathan Gair +5
We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian po…
GATSBI: Generative Adversarial Training for Simulation-Based Inference
Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts +4
Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generativ…
Variational methods for simulation-based inference
Manuel Glöckler, Michael Deistler, Jakob H. Macke
We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods. SNVI combines likelihood-estimation (or…