✳flâneur — a map of the web's best reading

Auto-Encoding Variational Bayes. Variational Inference | by Andac Demir | Medium

andacdemir-94699.medium.com · 893 words · saved by 1 readers

I realized there is a lack of educational resources that concentrate on the theory of variational inference in this platform. If you’re interested in the math behind variational inference, or more generally speaking probabilistic programming, you may find this post useful. According to Bayes rule, where p(z|x) denotes posterior, p(x|z) denotes likelihood and p(x) denotes evidence, also called marginal likelihood. Computing the posterior is known as inference, but inference in some cases is analytically intractable because evidence, which is represented by the integration ∫p(x,z)dz, is a very high dimensional distribution, and it’s hard to draw samples from. Additionally, even if we’re able to estimate evidence accurately, we might need to compute other integrals. For instance, KL Divergence Variational inference handles the posterior computation as an optimization problem. Specifically, we choose a member of a family q(z), which is the variational distribution, and minimize the KL dive

Auto-Encoding Variational Bayes Andac Demir 5 min read · Mar 1, 2021 -- 2 Listen Share Variational Inference I realized there is a lack of educational resources that concentrate on the theory of variational inference in this platform. If you’re interested in the math behind variational inference, or more generally speaking probabilistic programming, you may find this post useful. According to Bayes rule, Bayes Rule where p(z|x) denotes posterior , p(x|z) denotes likelihood and p(x) denotes evidence , also called marginal likelihood . Computing the posterior is known as inference , but inferenc

Explore this link on the map →

related reading