# Questions tagged [variational-inference]

For questions related to variational inference (VI), an optimization-based approach to the inference problem (i.e. the computation of the posterior given the prior, likelihood, and marginal). VI is used, for example, in the context of auto-encoders (VAEs) and Bayesian neural networks (BNNs).

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### Why don't we also need to approximate $p(x \mid z)$ in the VAE?

In the VAE, we approximate the probability distribution $p(z \mid x)$, where $z$ is the latent vector and $x$ is our data. The reason is that $p(z \mid x)$ becomes impossible to calculate for ...
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### Do we use two distinct layers to compute the mean and variance of a Gaussian encoder/decoder in the VAE?

I am looking at appendix C of the VAE paper: It says: C.1 Bernoulli MLP as decoder In this case let $p_{\boldsymbol{\theta}}(\mathbf{x} \mid \mathbf{z})$ be a multivariate Bernoulli whose ...
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### How does the VAE learn a joint distribution?

I found the following paragraph from An Introduction to Variational Autoencoders sounds relevant, but I am not fully understanding it. A VAE learns stochastic mappings between an observed $\mathbf{x}$...
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### Tensorflow Probability Implementation of Automatic Differentiation Variational Inference with Mixtures

In this paper, the authors suggest using the following loss instead of the traditional ELBO in order to train what basically is a Variational Autoencoder with a Gaussian Mixture Model instead of a ...
### What does the approximate posterior on latent variables, $q_\phi(z|x)$, tend to when optimising VAE's
The ELBO objective is described as follows $$ELBO(\phi,\theta) = E_{q_\phi(z|x)}[log p_\theta (x|z)] - KL[q_\phi (z|x)||p(z)]$$ This form of ELBO includes a regularisation term in the form of the ...
I'm trying to understand the concept of Variational Inference for BNNs. My source is this work. The aim is to minimize the divergence between the approx. distribution and the true posterior \text{KL}...