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 austinrochford.com
R² Priors for High-Dimensional Linear Regression and Autoregressive Timeseries in PyMC | Austin Rochford
| | phdinds-aim.github.io
Chapter 1: AutoRegressive Integrated Moving Average (ARIMA) - Time Series Analysis Handbook
5.7 parsecs away

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| | www.karsdorp.io
Population Size Estimation as a Regression Problem | Folgert Karsdorp
3.5 parsecs away

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| | I'm a researcher in Computational Humanities and Cultural Evolution at Amsterdam's [Meertens Institute](https://meertens.knaw.nl/index.php/en/), affiliated with the Royal Netherlands Academy of Arts and Sciences. I study aspects of cultural change and experiment with methods to quantify cultural diversity. A significant aspect of my recent work is understanding and accounting for biases in these quantifications. I like to use computational models from fields such as Machine Learning, Cultural Evolution, and Ecology to aid these investigations. Beyond research, I have a passion for teaching computer programming, especially within the Humanities context. Together with [Mike Kestemont](http://mikekestemont.github.io/) and [Allen Riddell](https://www.ariddell.or...
| | dfm.io
An astronomer's introduction to NumPyro | Dan Foreman-Mackey
2.6 parsecs away

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| | gregorygundersen.com
Hamiltonian Monte Carlo
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| [AI summary] Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo (MCMC) method that leverages Hamiltonian dynamics to generate samples from a probability distribution. Unlike traditional MCMC methods that rely on random walks, HMC introduces auxiliary momenta variables and simulates a physical system to produce correlated samples with higher efficiency. The method uses gradient information of the log density to guide the sampling process, enabling faster exploration of the target distribution and higher acceptance rates. The implementation of HMC involves defining the potential and kinetic energy functions, performing leapfrog integration to approximate the Hamiltonian dynamics, and using the Metropolis-Hastings acceptance criterion. An example using...