Episode
BITESIZE | How Do Diffusion Models Work?
- Podcast
- Learning Bayesian Statistics
- Published
- Feb 19, 2026
- Duration seconds
- 220
- Processing state
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Summary
Today's clip is from Episode 151 of the podcast, with Jonas Arruda In this conversation, Jonas Arruda explains how diffusion models generate data by learning to reverse a noise process. The idea is to start from a simple distribution like Gaussian noise and gradually remove noise until the target distribution emerges. This is done through a forward process that adds noise to clean parameters and a backward process that learns how to undo that corruption. A noise schedule controls how much noise is added or removed at each step, guiding the transformation from pure randomness back to meaningful structure. Get the full discussion here • Join this channel to get access to perks: https://www.patreon.com/c/learnbayesstats • Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302 • Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122 Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !