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Found 6 papers, 3 papers with code
Diffusion posterior sampling for simulation-based inference in tall data settings
Determining which parameters of a non-linear model could best describe a set of experimental data is a fundamental problem in science and it has gained much traction lately with the rise of complex large-scale simulators (a. k. a.
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators.
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Building upon the well-known classifier two-sample test (C2ST), we introduce L-C2ST, a new method that allows for a local evaluation of the posterior estimator at any given observation.
Validation Diagnostics for SBI algorithms based on Normalizing Flows
Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary complex and high-dimensional data distributions.
HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method.
Learning summary features of time series for likelihood free inference
There has been an increasing interest from the scientific community in using likelihood-free inference (LFI) to determine which parameters of a given simulator model could best describe a set of experimental data.
