Bayesian – Page 2 – 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: Topic Modeling

By Andrew Fairless on May 15, 2023March 30, 2023

https://maria-antoniak.github.io/2022/07/27/topic-modeling-for-the-people.html Topic Modeling for the PeopleMaria Antoniak “…I’m going to share a set of steps that you can follow to get coherent topics from most datasets. You can think ofContinue readingWhat I Read: Topic Modeling

What I Read: ELT Schedules, Root Cause Analysis

By Andrew Fairless on February 15, 2023December 16, 2022

https://www.montecarlodata.com/how-elt-schedules-can-improve-root-cause-analysis-for-data-engineers/ How ELT Schedules Can Improve Root Cause Analysis For Data EngineersRyan KearnsUpdated December 9, 2022 “In this article, Ryan Kearns… discusses the limitations of segmentation analysis when it comesContinue readingWhat I Read: ELT Schedules, Root Cause Analysis

What I Read: Bayesian Structural Timeseries

By Andrew Fairless on December 14, 2022November 5, 2022

Bayesian Structural Timeseries – ForecastingNathaniel FordeAug 15, 2022 “Bayesian structural timeseries assumes that the past is a guide to the future, and that if we can characterise the processes whichContinue readingWhat I Read: Bayesian Structural Timeseries

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: When use Bayesian optimization

By Andrew Fairless on October 10, 2022September 9, 2022

https://medium.com/@eduardogarrido90/when-do-i-need-to-use-bayesian-optimization-bo-series-i-50531ba6952d When do I need to use Bayesian optimization? (BO series I)Eduardo C. Garrido MerchánAug 22 “When do I need to use Bayesian optimization? And the answer is suprising, becauseContinue readingWhat I Read: When use Bayesian optimization

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: Estimating Model Performance

By Andrew Fairless on August 23, 2022July 18, 2022

https://pub.towardsai.net/estimating-model-performance-without-ground-truth-453b850dad9a Estimating Model Performance without Ground TruthIt’s possible, as long as you keep your probabilities calibratedMichał OleszakMay 12 “A model in production is like a baby: it needs to beContinue readingWhat I Read: Estimating Model Performance

What I Read: Bandits for Recommender Systems

By Andrew Fairless on June 8, 2022May 23, 2022

https://eugeneyan.com/writing/bandits/ Bandits for Recommender Systemseugeneyan “Recommender systems work well when we have a lot of data on user-item preferences…. Conversely, with very little data, we have low certainty…. Bandits addressContinue readingWhat I Read: Bandits for Recommender Systems

Tag: Bayesian