25 citations · 57 across the 18 of their papers we have counts for
5 papers · 1 filter
Simulation-Based Inference: A Practical Guide
Michael Deistler, Jan Boelts, Peter Steinbach +11
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers…
Multifidelity Simulation-based Inference for Computationally Expensive Simulators
Anastasia N. Krouglova, Hayden R. Johnson, Basile Confavreux +2
Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of deta…
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Richard Gao, Michael Deistler, Jakob H. Macke
Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive si…
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…
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…