Variational Inference

الاستدلال الاحتمالي المتغيِّر

إطار يُحوِّل مسألة الاستدلال البايزي غير القابل للحساب إلى مسألة أمثَلَة: نبحث عن أفضل تقريب q(z|x) للتوزيع البعدي الحقيقي بتصغير تباعد KL بينهما.

A framework that converts intractable Bayesian inference into an optimization problem: finding the best approximation q(z|x) to the true posterior by minimizing the KL divergence between them.

Also translated asالاستدلال التبايُني، الاستنتاج الاحتمالي المتغيِّر، الاستدلال المتغيّر

First appears in this corpus in: On Information and Sufficiency (1951)

Appears in these papers

Denoising Diffusion Implicit Models2021 in the sky ✦
Denoising Diffusion Probabilistic Models2020 in the sky ✦
Extracting and Composing Robust Features with Denoising Autoencoders2008 in the sky ✦
Maximum Likelihood from Incomplete Data via the EM Algorithm1977 in the sky ✦
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images1984 in the sky ✦
On Information and Sufficiency1951 in the sky ✦
Latent Dirichlet Allocation2003 in the sky ✦
Latent Dirichlet Allocation2003 in the sky ✦
Variational Inference with Normalizing Flows2015 in the sky ✦
Variational Inference with Normalizing Flows2015 in the sky ✦
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning1992 in the sky ✦
Auto-Encoding Variational Bayes2013 in the sky ✦
Auto-Encoding Variational Bayes2013 in the sky ✦