Oct 18, 2024 · We propose predictive variational inference (PVI): a general inference framework that seeks and samples from an optimal posterior density.
Oct 18, 2024 · We propose predictive variational inference (PVI): a general inference framework that seeks and samples from an optimal posterior density.
Oct 24, 2024 · We propose predictive variational inference (PVI): a general inference framework that seeks and samples from an optimal posterior density such ...
This framework aims to generate more accurate posterior distributions and enhance predictive performance, particularly in scenarios where the underlying model ...
Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not ...
Oct 21, 2024 · The paper introduces predictive variational inference, a new framework for learning the posterior distribution in a way that optimizes for predictive ...
[2024] Predictive variational inference: Learn the predictively optimal posterior distribution. ... [2023] Variational inference with Gaussian score matching.
Jun 16, 2019 · Once you have a posterior distribution you can maximise what you like, for example my choosing a MAP estimate of the parameter, but there's ...
Missing: predictively optimal
Fitting approximate posteriors with variational inference transforms the inference problem into an optimization problem, where the goal is (typically) to ...
The posterior distribution is virtually impossible to represent and solve in high dimensional problems like over-parameterized deep learning. ▫ VI approximates ...
Missing: predictively | Show results with:predictively