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In variational inference, the original objective is to minimize the Kullback-Leibler divergence between the variational distribution, $q(z \mid x)$, and the posterior, $p(z \mid x) = \frac{p(x, z)}{\int_z p(x, z)}$, given that the posterior can be difficult to directly infer with the Bayes rule, due to the denominator term, which can contain an intractable ...


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From this document, as you found here, $X$ is an observed variable and $Z$ is a hidden variable; $p(X)$ is the density function of $X$. The posterior distribution of the hidden variables can then be written as follows using the Bayes’ Theorem: $$p(Z|X) = \frac{p(X|Z)p(Z)}{p(X)} = \frac{p(X|Z)p(Z)}{\int_Zp(X,Z)}$$ Now base on what you post, if we denote ...


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The use of KL provides a more intuitive way of what the ELBO is attempting to maximize. Basically, we want to find a posterior approximation such that $p(z\mid x) \approx q(z)\in\mathcal{Q}$ $$KL(q(z)\parallel p(z\mid x)) \rightarrow \min_{q(z)\in\mathcal{Q}}$$ As a result of this, while finding this optimal posterior approximation, we maximize the ...


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A good example is the degree of freedom in Student's distribution: ‌ The degrees of freedom refers to the number of independent observations in a set of data. For example: When estimating a mean score or a proportion from a single sample, the number of independent observations is equal to the sample size minus one. e.g, if we have 100 observation $X_1, \...


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I will try to answer your questions directly (but I guess I won't be able to), otherwise, this can become quite confusing, given the inconsistencies that can be found across different sources. In $logp_{\theta}(x^1,...,x^N)=D_{KL}(q_{\theta}(z|x^i)||p_{\phi}(z|x^i))+\mathbb{L}(\phi,\theta;x^i)$ why is $\theta$ and param for $p$ and $q$? In a few words, ...


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Here's a link to my answer on CV Stack Exchange, where I have mentioned about latent spaces and some deep learning models that learn these representations: https://stats.stackexchange.com/questions/442352/what-is-a-latent-space/442360#442360 In short, deep learning models for Domain Adaptation, Computer Vision, Natural Language Processing, Recommendation ...


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