Explore >> Select a destination
|
You are here 
|
austinrochford.com | ||
| | | | |
twiecki.io
|
|
| | | | | [AI summary] This technical blog post explains the advantages of hierarchical Bayesian modeling over non-hierarchical approaches using a case study of predicting radon levels across different US counties with the PyMC3 library. | |
| | | | |
www.karsdorp.io
|
|
| | | | | 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... | |
| | | | |
www.djmannion.net
|
|
| | | | | Data are sometimes on a circular scale, such as the angle of an oriented stimulus, and the analysis of such data often needs to take this circularity into account. Here, we will look at how we can use PyMC to fit a model to circular data. | |
| | | | |
www.quantstart.com
|
|
| | | Bayesian Statistics: A Beginner's Guide | ||
