Compositional Score Modeling for Simulation-based Inference
This work addresses a bottleneck in simulation-based inference for researchers dealing with multiple observations, offering a more efficient alternative to current methods.
The paper tackles the inefficiency of existing simulation-based inference methods when conditioning on multiple observations by introducing a conditional score modeling approach that combines learned scores from individual observations to sample from the target posterior, achieving sample efficiency and avoiding drawbacks of standard inference methods.
Neural Posterior Estimation methods for simulation-based inference can be ill-suited for dealing with posterior distributions obtained by conditioning on multiple observations, as they tend to require a large number of simulator calls to learn accurate approximations. In contrast, Neural Likelihood Estimation methods can handle multiple observations at inference time after learning from individual observations, but they rely on standard inference methods, such as MCMC or variational inference, which come with certain performance drawbacks. We introduce a new method based on conditional score modeling that enjoys the benefits of both approaches. We model the scores of the (diffused) posterior distributions induced by individual observations, and introduce a way of combining the learned scores to approximately sample from the target posterior distribution. Our approach is sample-efficient, can naturally aggregate multiple observations at inference time, and avoids the drawbacks of standard inference methods.