ELBO
Chapter 14: Information Theory — Evidence Lower Bound (also appears in Ch. 16)
From the book
Chapter 14: Information Theory. In the chapter mind map this icon labels Evidence Lower Bound. The discussion below is excerpted and lightly edited from § Definition: Evidence Lower Bound in Mathematics for AI and Machine Learning. Related material also appears in Chapter 16 (Variational Inference:ELBO Maximization).
For a probabilistic model with observed data $\mathbf x$, latent variables $\mathbf z$, and parameters $\theta$, the evidence (marginal likelihood) is
where $q(\mathbf z)$ is a variational distribution approximating the true posterior $p_{\boldsymbol\theta}(\mathbf z | \mathbf x)$.
What this drawing shows
What you see. Represents the evidence lower bound used in variational inference and latent-variable models.
In the mind map. Chapter 14 — Evidence Lower Bound. See From the book above for definitions, figures, and worked examples.
Also appears in Ch. 16 (Variational Inference:ELBO Maximization).
Where to read next
Read the full definitions, figures, and worked examples in Chapter 14: Information Theory — see the mind-map node Evidence Lower Bound.
This concept is also referenced in Chapter 16 (Variational Inference:ELBO Maximization).