Understanding the Evidence Lower Bound (ELBO) - Cross Validated
2022年6月24日 · With that in mind, the ELBO can be a meaningful lower bound on the log-likelihood: both are negative, but ELBO is lower. How much lower? The KL divergence from the conditional distribution. I don’t see where you think the figure is indicating that it should be positive. The bottom of the diagram isn’t 0.
How does maximizing ELBO in Bayesian neural networks give us …
2022年10月1日 · Here is my question: how does maximizing ELBO lead to a good/correct posterior predictive distribution ...
maximum likelihood - ELBO - Jensen Inequality - Cross Validated
2024年1月22日 · ELBO is a quantity used to approximate the log marginal likelihood of observed data, after applying ...
maximum likelihood - VQ-VAE objective - is it ELBO maximization, …
2022年10月19日 · $\begingroup$ thanks! so if the ELBO itself is tractable - why does rocca show that we are optimizing the KL divergence? he shows that we can develop the KL divergence between the approximate posterior and the true posterior (which is indeed unknown) as a sum of the data likelihood and the KL divergence between the approximate posterior and the prior, …
Is value of ELBO a scalar or a distribution? - Cross Validated
2018年1月3日 · Technically ELBO would be a functional, a function that takes a function as an argument. However, in practice most problems assume some class of distributions (e.g. Gaussian, Gamma, etc), which eliminates the functional aspect of the problem and then optimize within this class of distributions, making the problem a single variable, or ...
formulation of evidence lower bound (ELBO) of the log likelihood
2022年4月9日 · Tour Start here for a quick overview of the site
Calculating ELBO in EM algorithm - Cross Validated
2020年10月18日 · Calculating ELBO in EM algorithm. Ask Question Asked 4 years, 5 months ago. Modified 4 years, 5 months ago.
neural networks - ELBO maximization with SGD - Cross Validated
2020年2月12日 · Maximizing the ELBO, however, does have analytical update formulas (i.e. formulas for the E and M steps). I understand why in this case maximizing the ELBO is a useful approximation. However, in more complex models, such as VAE, the E & M steps themselves don't have a closed solution, and ELBO maximization is done with SGD.
Variational Inference: Computation of ELBO and CAVI algorithm
2018年10月2日 · I am reading/studying this paper 1 and got confused with some expressions. It might be basic for many of you, so my apologizes. In the paper the following prior model is assumed: $\\mu_k \\sim \\mat...
Gradients of KL divergence and ELBO for variational inference
2019年10月25日 · The ELBO $\mathcal{L}(\phi)$ can be written as the difference between the log evidence and the KL divergence between the variational distribution and true posterior: $$\mathcal{L}(\phi) = \log p(x) - D_{KL} \Big( q_\phi(\theta) \parallel p(\theta \mid x) \Big)$$ Take the gradient of both sides w.r.t. the variational parameters.