When speaking about Bayesian statistics, we often hear about « probabilistic programming » — but what is it? Which languages and libraries allow you to program probabilistically? When is Stan, PyMC, Pyro or any other probabilistic programming language most appropriate for your project? And when should you even use Bayesian libraries instead of non-bayesian tools, like Statsmodels or Scikit-learn?


Colin Carroll will answer all these questions for you. Colin is a machine learning researcher and software engineer who’s notably worked on modeling risk in the airline industry and building NLP-powered search infrastructure for finance. He’s also an active contributor to open source, particularly to the popular PyMC3 and ArviZ libraries.


Having studied geometric measure theory at Rice University, Colin was bound to walk in the woods with Pete the pup – who was there when we recorded by the way – and to launch balloons into near-space in his spare time.


Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at [ Ссылка ]!


Links from the show:

• Colin's blog: [ Ссылка ]
• Colin on Twitter: [ Ссылка ]
• Colin on GitHub: [ Ссылка ]
• Very parallel MCMC sampling: [ Ссылка ]
• A tour of probabilistic programming APIs: [ Ссылка ]
• PyMC3, Probabilistic Programming in Python: [ Ссылка ]
• Stan: [ Ссылка ]
• Pyro, Deep Universal Probabilistic Programming: [ Ссылка ]
• ArviZ, Exploratory analysis of Bayesian models: [ Ссылка ]
• PyMC-Learn, Probabilistic models for machine learning: [ Ссылка ]
• Facebook’s Prophet uses Stan: [ Ссылка ]
• Prophet in PyMC3: [ Ссылка ]

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