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Introduction to Variational Inference By Automatic Differentiation In Tensorflow Probability
We find a surrogate posterior by maximizing the Evidence Lower Bound (ELBO). With a proposal distribution, this can be solved ... In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ... This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check ... David Blei, Rajesh Ranganath, Shakir Mohamed. One of the core problems of modern statistics and machine learning is to ... MLFoundations In this video, we use a hands-on code demo in This is Lecture 23 of the course on Probabilistic Machine Learning in the Summer Term of 2025 at the University of Tübingen, ...
This is the twentyfourth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig in the Summer Term 2023 at the University ... This is the twentyfourth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the ... www.pydata.org When Bayesian modeling scales up to large datasets, traditional MCMC methods can become impractical due to ... For more information about Stanford's Artificial Intelligence programs visit: To follow along with the course, ...
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Variational Inference by Automatic Differentiation in TensorFlow Probability
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Last Updated: May 22, 2026
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