posterior – Andrew Fairless, Ph.D.

What I Read: different meanings of p-value

By Andrew Fairless on June 7, 2023May 1, 2023

4 different meanings of p-value (and how my thinking has changed) 4 different meanings of p-value (and how my thinking has changed)Posted on April 14, 2023 9:14 AMAndrew Gelman “TheContinue readingWhat I Read: different meanings of p-value

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: introduction to NumPyro

By Andrew Fairless on September 19, 2022August 22, 2022

https://dfm.io/posts/intro-to-numpyro/ An astronomer’s introduction to NumPyroDan Foreman-MackeyJul 28 2022 “In this post… I’ll focus primarily on providing an introduction to NumPyro, which is a probabilistic programming library that provides anContinue readingWhat I Read: introduction to NumPyro

What I Read: Short Time Series

By Andrew Fairless on September 1, 2022August 3, 2022

https://minimizeregret.com/short-time-series-prior-knowledge Modeling Short Time Series with Prior KnowledgeWhat ‘Including Prior Information’ really looks like.Tim Radtke2019-04-16 “It is generally difficult to model time series when there is insuffient data to modelContinue readingWhat I Read: Short Time Series

What I Read: Bayesian Geometry

By Andrew Fairless on February 21, 2022January 20, 2022

https://towardsdatascience.com/on-bayesian-geometry-7755abf9f4d2 On Bayesian GeometryGeometric interpretation of probability distributionsMichael Larionov, PhD “Bayesian inference is based on the fact that we often don’t know the underlying distribution of data, so we needContinue readingWhat I Read: Bayesian Geometry

What I Read: How to assign partial credit on an exam of true-false questions

By Andrew Fairless on November 30, 2021November 16, 2021

https://terrytao.wordpress.com/2016/06/01/how-to-assign-partial-credit-on-an-exam-of-true-false-questions/ How to assign partial credit on an exam of true-false questions?By Terence Tao1 June, 2016Updates on my research and expository papers, discussion of open problems, and other maths-related topics.Continue readingWhat I Read: How to assign partial credit on an exam of true-false questions

What I Read: Bayesian and frequentist results

By Andrew Fairless on May 21, 2021May 1, 2021

https://www.allendowney.com/blog/2021/04/25/bayesian-and-frequentist-results-are-not-the-same-ever/ Bayesian and frequentist results are not the same, everApril 25, 2021Allen Downey “You can’t compare results from Bayesian and frequentist methods because the results are different kinds of things….Continue readingWhat I Read: Bayesian and frequentist results

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 Read: The Case for Bayesian Deep Learning

By Andrew Fairless on December 6, 2020December 13, 2020

https://cims.nyu.edu/~andrewgw/caseforbdl/ The Case for Bayesian Deep LearningAndrew Gordon WilsonJanuary 11, 2020 “…ignoring epistemic uncertainty is a key reason that standard neural network training is miscalibrated. By erroneously assuming that theContinue readingWhat I Read: The Case for Bayesian Deep Learning

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