Bayes Theorem – Andrew Fairless, Ph.D.

What I Read: How diffusion models work

By Andrew Fairless on November 30, 2022October 20, 2022

https://theaisummer.com/diffusion-models/ How diffusion models work: the math from scratchSergios Karagiannakos, Nikolas Adaloglouon2022-09-29 “Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images.”

What I Read: Generative Modeling by Estimating Gradients

By Andrew Fairless on March 7, 2022January 20, 2022

https://yang-song.github.io/blog/2021/score/ Generative Modeling by Estimating Gradients of the Data DistributionAuthor Yang SongDate May 5, 2021 “This blog post gives a detailed introduction to score-based generative models. We demonstrate that thisContinue readingWhat I Read: Generative Modeling by Estimating Gradients

What I Read: Bayesian Hierarchical Modelling at Scale

By Andrew Fairless on April 2, 2021March 21, 2021

https://florianwilhelm.info/2020/10/bayesian_hierarchical_modelling_at_scale/ Finally! Bayesian Hierarchical Modelling at ScaleFlorian WilhelmDate Oct. 01, 2020 “In this blog post, I want to draw your attention to the somewhat dusty Bayesian Hierarchical Modelling. Modern techniquesContinue readingWhat I Read: Bayesian Hierarchical Modelling at Scale

What I Read: Neural Text Generation

By Andrew Fairless on March 11, 2021March 7, 2021

https://lilianweng.github.io/lil-log/2021/01/02/controllable-neural-text-generation.html Controllable Neural Text GenerationJan 2, 2021Lilian Weng “How to steer a powerful unconditioned language model? In this post, we will delve into several approaches for controlled content generation withContinue readingWhat I Read: Neural Text Generation

What I Watch: The medical test paradox, Can redesigning Bayes rule help?

By Andrew Fairless on February 28, 2021February 27, 2021

https://youtu.be/lG4VkPoG3ko The medical test paradox: Can redesigning Bayes rule help?Dec 22, 20203Blue1Brown “In short, an accurate test is not necessarily a predictive test… of all of the places for mathContinue readingWhat I Watch: The medical test paradox, Can redesigning Bayes rule help?

What I Read: Think Like an Epidemiologist

By Andrew Fairless on January 16, 2021December 24, 2020

https://www.nytimes.com/2020/08/04/science/coronavirus-bayes-statistics-math.html How to Think Like an EpidemiologistDon’t worry, a little Bayesian analysis won’t hurt you.By Siobhan RobertsAug. 4, 2020Updated 4:57 p.m. ET “Observing early research in March about how theContinue readingWhat I Read: Think Like an Epidemiologist

What I Watch: Probabilities of probabilities binomial

By Andrew Fairless on December 18, 2020December 4, 2020

Which rating is better, mathematically speaking? | Probabilities of probabilities, part 1Mar 15, 20203Blue1Brown

What I Read: Replacing do-calculus with Bayes rule

By Andrew Fairless on December 8, 2020November 29, 2020

https://arxiv.org/abs/1906.07125 Replacing the do-calculus with Bayes ruleFinnian Lattimore, David Rohde “…while it is critical to explicitly model our assumptions on the impact of intervening in a system, provided we doContinue readingWhat I Read: Replacing do-calculus with Bayes rule

What I Read: Bayesian Neural Networks Need Not Concentrate

By Andrew Fairless on December 7, 2020December 13, 2020

https://jacobbuckman.com/2020-01-22-bayesian-neural-networks-need-not-concentrate/ Bayesian Neural Networks Need Not ConcentratePosted on January 22, 2020by Carles Gelada and Jacob Buckman “…it’s important that we, as a field, stop ignoring the crucial importance that theContinue readingWhat I Read: Bayesian Neural Networks Need Not Concentrate

What I Watch: Bayes theorem

By Andrew Fairless on November 28, 2020November 28, 2020

Bayes theoremDec 22, 20193Blue1Brown “The goal is for you to come away from this video understanding one of the most important formulas in all of probability: Bayes theorem.”

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