How do you handle your MCMC samples once your Bayesian model fit properly? Which diagnostics do you check to see if there was a computational problem? And isn’t that nice when you have beautiful and reliable plots to complement your analysis and better understand your model?
I know what you think: plotting can be long and complicated in these cases. Well, not with ArviZ, a platform-agnostic package to do exploratory analysis of your Bayesian models. And in this episode, Ari Hartikainen will tell you why.
Ari is a data-scientist in geophysics and a researcher at the Department of Civil Engineering of Aalto University in Finland. He mainly works on geophysics, Bayesian statistics and visualization.
Ari’s also a prolific open-source contributor, as he’s a core-developer of the popular Stan and ArviZ libraries. He’ll tell us how PyStan interacts with ArviZ, what he thinks ArviZ most useful features are, and which common difficulties he encounters with his models and data.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Links from the show:
- Ari on GitHub: https://github.com/ahartikainen
- Ari on Twitter: https://twitter.com/a_hartikainen
- ArviZ -- Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/
- Introductory paper of ArviZ in The Journal of Open Source Software: https://www.researchgate.net/publication/330402908_ArviZ_a_unified_library_for_exploratory_analysis_of_Bayesian_models_in_Python
- Stan -- Statistical Modeling Platform: https://mc-stan.org/
- GPflow -- Gaussian processes in TensorFlow: https://www.gpflow.org/
- GPy -- Gaussian processes framework in Python: https://sheffieldml.github.io/GPy/