# Questions tagged [variational-bayes]

Variational Bayesian methods approximate intractable integrals found in Bayesian inference and machine learning. Primarily, these methods serve one of two purposes: Approximating the posterior distribution, or bounding the marginal likelihood of observed data.

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### Reparametrization trick in VAE at Evaluation time

So I've been trying to implement the Variational Auto-Encoder model of Kigma et.al, but something has been bugging me. While I understand the need for reparametrization trick at training time, the ...
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### Computing the variational distribution of a softmax function

I am trying to compute the variational approximate distribution of the following softmax function \begin{equation} \mathbb{E}_{q(\mathbf{s}_{t-1})q(\mathbf{x}_t)}\big[P(\mathbf{s}_{t}=i|\mathbf{s}...
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### Why using variational inference to do minimization? [closed]

I'm reading a paper on conditoinal random fields. They arrive at a formulation for energy, and they go like this: "minimizing this is intractable" What does that mean? I heard about intractable ...
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### Why do we penalize individual example divergence in variational autoencoder?

In variational autoencoder, we want to learn a mapping between input space $X$ and latent space $Z$, and $z\in Z$ is related to $x\in X$ with $z\sim MVN(\mu(x), \Sigma(x))$. In addition, we desire ...
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### Need some help understanding the factorised posterior in semi-supervised generative modelling

I am having a bit of trouble with the derivation in Kingma's semi-supervised generative modelling paper for the M2-model. The M2 model assumes a probabilistic model where the data $x$ is generated by ...
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### VRNN (Variational Recurrent Neural Network) code with Variable Input Lengths on Tensorflow

I have been trying to write VRNN (Variational Recurrent Neural Network: A recurrent latent variable model for sequential data (NIPS 2015)) with variable input data length. My problem is that it is ...
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### What is the difference between approximate bayesian computation vs approximate bayesian inference?

What are the main differences between approximate bayesian computation vs approximate bayesian inference? Are they essentially the same? Do they refer to the same of different family of models? My ...
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### Evaluating General Variational Inference Local and Global Updates

For coordinate ascent variational inference (CAVI) we can iterate between the "local" parameter updates and the "global" parameter updates. These can be expressed (following [A]) for exponential ...
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### Obtaining VAE reconstruction probability

How does one calculate the reconstruction probability? Let's look at the keras example code from here. Is the reconstruction probability the output of a specific layer, or is it to be calculated ...
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### How can we cast an optimisation problem as an inference problem?

The main idea of variational methods is to cast inference as an optimisation problem. In the paper Junction Tree Variational Autoencoder for Molecular Graph Generation, the authors state that the ...