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 www.ethanrosenthal.com
Time Series for scikit-learn People (Part II): Autoregressive Forecasting Pipelines | Ethan Rosenthal
| | matbesancon.xyz
A Pythonic data science project: Part III |
3.8 parsecs away

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| | Learning by doing: predicting the outcome.
| | dfm.io
Autocorrelation time estimation | Dan Foreman-Mackey
4.7 parsecs away

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| | [AI summary] This document provides a comprehensive guide to estimating autocorrelation times in Markov Chain Monte Carlo (MCMC) simulations. It begins by explaining the importance of autocorrelation in MCMC and how it affects the effective sample size. The text then introduces several methods for estimating autocorrelation times, including the Goodman & Weare (2010) method and a newer algorithm developed by the author (DFM 2017). The document also discusses the limitations of these methods with short chains and introduces a maximum likelihood approach using the celerite library to fit an autocorrelation model. Finally, it concludes with recommendations for choosing appropriate chain lengths based on the estimated autocorrelation times.
| | lukesingham.com
Binary Classification in Python - Who's Going to Leave Next?
2.3 parsecs away

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| | This post goes through a binary classification problem with Python's machine learning library scikit-learn.
| | fharrell.com
My Journey from Frequentist to Bayesian Statistics - Statistical Thinking
20.7 parsecs away

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| This is the story of what influenced me to become a Bayesian statistician after being trained as a classical frequentist statistician, and practicing only that mode of statistics for many years.