Scaling Probabilistic Models With Variational Inference

May 16, 2026
Media Summary: Recorded at PyData Berlin 2025, Learn how to In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ... Details *** Sorry, this event has been postponed one week to June 6, 2023 *** Topic: We will finish our discussion of

Scaling Probabilistic Models With Variational Inference - Detailed Analysis & Overview

Recorded at PyData Berlin 2025, Learn how to In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ... Details *** Sorry, this event has been postponed one week to June 6, 2023 *** Topic: We will finish our discussion of Course Link: Welcome to week five of our course. For more information about Stanford's Artificial Intelligence programs visit: To follow along with the course, ... We find a surrogate posterior by maximizing the Evidence Lower Bound (ELBO). With a proposal distribution, this can be solved ...

In our paper represent algorithms for performing 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 ... ... now paper represent algorithms for performing

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Scaling Probabilistic Models with Variational Inference
Variational Inference - Explained
Scaling Bayesian Inference: The Power of Amortized Variational Inference
Variational Inference and Optimization 2 by Helge Langseth and Thomas D. Nielsen
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Probabilistic ML — Lecture 24 — Variational Inference
Probabilistic ML - Lecture 24 - Variational Inference
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Variational Inference and Probabilistic Programming II by Andrés R. Masegosa & Thomas D. Nielsen

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Scaling Probabilistic Models with Variational Inference

Scaling Probabilistic Models with Variational Inference

Recorded at PyData Berlin 2025, https://2025.pycon.de/program/BCGJQB/ Learn how to

Variational Inference - Explained

Variational Inference - Explained

In this video, we break down

Scaling Bayesian Inference: The Power of Amortized Variational Inference

Scaling Bayesian Inference: The Power of Amortized Variational Inference

This video explores Amortized

Variational Inference and Optimization 2 by Helge Langseth and Thomas D. Nielsen

Variational Inference and Optimization 2 by Helge Langseth and Thomas D. Nielsen

Nordic

Variational Inference | Evidence Lower Bound (ELBO) | Intuition & Visualization

Variational Inference | Evidence Lower Bound (ELBO) | Intuition & Visualization

In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ...

Advanced Probabilistic Machine Learning -- Variational Inference

Advanced Probabilistic Machine Learning -- Variational Inference

Details *** Sorry, this event has been postponed one week to June 6, 2023 *** Topic: We will finish our discussion of

Variational Autoencoders - Part 1 (Scaling Variational Inference & Unbiased estimates)

Variational Autoencoders - Part 1 (Scaling Variational Inference & Unbiased estimates)

Course Link: https://www.coursera.org/learn/bayesian-methods-in-machine-learning Welcome to week five of our course.

Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11

Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, ...

Probabilistic ML — Lecture 24 — Variational Inference

Probabilistic ML — Lecture 24 — Variational Inference

This is the twentyfourth lecture in the

Probabilistic ML - Lecture 24 - Variational Inference

Probabilistic ML - Lecture 24 - Variational Inference

This is the twentyfourth lecture in the

Nick Mancuso | Variational Inference for large-scale genomic data | CGSI 2022

Nick Mancuso | Variational Inference for large-scale genomic data | CGSI 2022

Scaling probabilistic models

Variational Inference and Probabilistic Programming II by Andrés R. Masegosa & Thomas D. Nielsen

Variational Inference and Probabilistic Programming II by Andrés R. Masegosa & Thomas D. Nielsen

The tutorial "

MLSS 2019 David Blei: Variational Inference: Foundations and Innovations (Part 1)

MLSS 2019 David Blei: Variational Inference: Foundations and Innovations (Part 1)

David Blei Topic:

Variational Inference by Automatic Differentiation in TensorFlow Probability

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 ...

Variational Inference in Mixed Probabilistic Submodular Models

Variational Inference in Mixed Probabilistic Submodular Models

In our paper represent algorithms for performing

Probabilistic ML - 23 - Variational Inference

Probabilistic ML - 23 - Variational Inference

This is Lecture 23 of the course on

Variational Methods: How to Derive Inference for New Models (with Xanda Schofield)

Variational Methods: How to Derive Inference for New Models (with Xanda Schofield)

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 ...

Variational Inference in Mixed Probabilistic Submodular Models

Variational Inference in Mixed Probabilistic Submodular Models

... now paper represent algorithms for performing