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 do we use $q_{\phi}(z \mid x^{(i)})$ in the objective function of amortized variational inference, while sometimes we use $q(z)$?
In page 21 here, it states:
General Idea of Amortization: if same inference problem needs to be solved many times, can we parameterize a neural network to solve it?
Our case: for all $x^{(i)}$ we ...
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What is $p(Z)$ and what happens to the variational posterior $q(Z;X)$ during data synthesis (after training)?
From my understanding of inference problems, we want to compute the posterior $p(Z|X=D)$, for some observed dataset $D=(x^1, x^2,\dots,x^n)$ of $n$ independent observations, in order to "update&...
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what's the best way of Inferring probability chance of heads with a coin of Unknown Bias that changes regularly
hi what would be the best strategy to infer the range of probabilities of getting heads with a coin of Unknown Bias that is variable? I'm working on a similar problem with a game AI.
I'm working on a ...
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Why isn't the evidence $p(x) = 1$ if it's an observed variable?
Every explanation of variational inference starts with the same basic premise: given an observed variable $x$, and a latent variable $z$,
$$ p(z|x)=\frac{p(x,z)}{p(x)} $$
and then proceeds to expand $...
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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 ...
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