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Bookmark or Share- The evidence lower bound (ELBO) - Matthew N. BernsteinThe evidence lower bound is an important quantity at the core of a number of important algorithms used in statistical inference including expectation-maximization and variational inference. In this post, I describe its context, definition, and derivation.
- ELBO — What & Why | Yunfan’s BlogELBO (evidence lower bound) is a key concept in Variational Bayesian Methods. It transforms inference problems, which are always intractable, into optimization problems that can be solved with, for example, gradient-based methods.
- How I learned to stop worrying and write ELBO (and its ... - Yuge ShiThis work provides a very intuitive perspective of the VAE objective by decomposing and rewriting ELBO. For a batch of N observations \(X=\{x_n\}_{n=1}^N\) and their corresponding latent codes \(Z=\{z_n\}_{n=1}^N\), ELBO can be rewritten as:
- The ELBO in Variational Inference - Gregory GundersenI derive the evidence lower bound (ELBO) in variational inference and explore its relationship to the objective in expectation–maximization and the variational autoencoder. Published 16 April 2021
- Evidence, KL-divergence, and ELBO - mpatacchiola’s blogEvidence, KL-divergence, and ELBO. This post is the first of a series on variational inference, a tool that has been widely used in machine learning. My goal here is to define the problem and then introduce the main characters at play: evidence, Kullback-Leibler (KL) divergence, and Evidence Lower BOund (ELBO).
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