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 scipy.github.io
Quasi-Monte Carlo submodule (scipy.stats.qmc) - SciPy v1.18.0.dev Manual
| | dfm.io
Autocorrelation time estimation | Dan Foreman-Mackey
5.6 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.
| | darrenjw.wordpress.com
Unbiased MCMC with couplings - Darren Wilkinson's blog
5.0 parsecs away

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| | Yesterday there was an RSS Read Paper meeting for the paper Unbiased Markov chain Monte Carlo with couplings by Pierre Jacob, John O'Leary and Yves F. Atchadé. The paper addresses the bias in MCMC estimates due to lack of convergence to equilibrium (the "burn-in" problem), and shows how it is possible to modify MCMC algorithms...
| | www.huber.embl.de
Modern Statistics for Modern Biology - 4 Mixture Models
5.5 parsecs away

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| | If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book.
| | www.unofficialgoogledatascience.com
Using random effects models in prediction problems
26.8 parsecs away

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| by NICHOLAS A. JOHNSON, ALAN ZHAO, KAI YANG, SHENG WU, FRANK O. KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction ...