# BITESIZE | How Do Diffusion Models Work?

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Text version: https://stenobird.com/podcast/learning-bayesian-statistics/bitesize-how-do-diffusion-models-work.md
Podcast: [Learning Bayesian Statistics](https://stenobird.com/podcast/learning-bayesian-statistics)
Published: 2026-02-19T18:15:00+00:00
Episode link: https://api.riverside.com/hosting-analytics/media/41c07d4ddd3c841dc2b98cb0a717eb30cfa949619b42c19b5ec7942e39e71141/eyJlcGlzb2RlSWQiOiJlODZhNGEwMi1hNDFlLTQ3NmMtYTM1MS1mMGM4M2VlNjJjMmMiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjk5NzRmODczNjM3MmEyZGQ1NTg1NGViL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi0yLTE5X18xOC01OS0zNS5tcDMifQ==.mp3
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Duration seconds: 220

## Resource

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/ !

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## Transcript

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