Search Results for author:
Found 4 papers, 2 papers with code
Minimizing $f$-Divergences by Interpolating Velocity Fields
Wasserstein Gradient Flow can move particles along a path that minimizes the $f$-divergence between the target and particle distributions.
Robust Neural Posterior Estimation and Statistical Model Criticism
In this work we revisit neural posterior estimation (NPE), a class of algorithms that enable black-box parameter inference in simulation models, and consider the implication of a simulation-to-reality gap.
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
We embed the model into a sequential training procedure, which guides simulations using the current approximation of the posterior at the observation of interest, thereby reducing the simulation cost.
Variational Likelihood-Free Gradient Descent
In many scientific applications, we do not have explicit access to the likelihood function.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
