Ari Hartikainen's guest appearances

Ari Hartikainen

Learning Bayesian Statistics

David Spiegelhalter

Andrew Gelman

Michael Lee

Baba Brinkman

Michael Kennedy

Allen Downey

Sean Taylor

Michael Faber

Matthew Kay

Michael Betancourt

Eric Ma

Karin Knudson

Colin Carroll

Osvaldo Martin

Chris Fonnesbeck

OwlTail

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/ahartikainenAri on Twitter: https://twitter.com/a_hartikainenArviZ -- 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_PythonStan -- 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/This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

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 <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !Links from the show:<ul><li>Ari on GitHub: <a href="https://github.com/ahartikainen" rel="noopener noreferrer nofollow" target="_blank">https://github.com/ahartikainen</a></li><li>Ari on Twitter: <a href="https://twitter.com/a_hartikainen" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/a_hartikainen</a></li><li>ArviZ -- Exploratory analysis of Bayesian models: <a href="https://arviz-devs.github.io/arviz/" rel="noopener noreferrer nofollow" target="_blank">https://arviz-devs.github.io/arviz/</a></li><li>Introductory paper of ArviZ in The Journal of Open Source Software: <a href="https://www.researchgate.net/publication/330402908_ArviZ_a_unified_library_for_exploratory_analysis_of_Bayesian_models_in_Python" rel="noopener noreferrer nofollow" target="_blank">https://www.researchgate.net/publication/330402908_ArviZ_a_unified_library_for_exploratory_analysis_of_Bayesian_models_in_Python</a></li><li>Stan -- Statistical Modeling Platform: <a href="https://mc-stan.org/" rel="noopener noreferrer nofollow" target="_blank">https://mc-stan.org/</a></li><li>GPflow -- Gaussian processes in TensorFlow: <a href="https://www.gpflow.org/" rel="noopener noreferrer nofollow" target="_blank">https://www.gpflow.org/</a></li><li>GPy -- Gaussian processes framework in Python: <a href="https://sheffieldml.github.io/GPy/" rel="noopener noreferrer nofollow" target="_blank">https://sheffieldml.github.io/GPy/</a></li></ul>This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

#10 Exploratory Analysis of Bayesian Models, with ArviZ and Ari Hartikainen