Speaker
Description
Simulation-based inference (SBI) has seen remarkable development in recent years and has found widespread application across a range of physical sciences. A defining characteristic of SBI is its ability to perform likelihood-free inference, relying on simulators rather than explicit likelihood functions. Several representative methods have emerged within this framework, such as Approximate Bayesian Computation (ABC), Neural Posterior Estimation (NPE), and Neural Ratio Estimation (NRE). In this work, we present a variant of the SNPE family—One Shot Simulation-based Inference—which leverages Normalizing Flows to directly approximate the true posterior distribution with a trainable neural network. In parallel, we introduce a second network that learns an adaptive proposal distribution, which generates increasingly informative samples during training. This design eliminates the need for multiple inference rounds, significantly accelerates convergence, and reduces the overall computational cost. To demonstrate the effectiveness of this approach, we will show the application results of several model cases.
| AI keywords | simulation-based inference, normalizing-sequential flow, probability density estimate |
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