Probabilistic Modeling In Python (And What That Even Means)
Most programming is deterministic, relying on concrete logic to determine the way that it operates. However, there are problems that require a way to work with uncertainty. PyMC3 is a library designed for building models to predict the likelihood of certain outcomes. In this episode Thomas Wiecki explains the use cases where Bayesian statistics are necessary, how PyMC3 is designed and implemented, and some great examples of how it is being used in real projects.
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- Your host as usual is Tobias Macey and today I’m interviewing Thomas Wiecki about PyMC3, a project for probabilistic programming in Python
- How did you get introduced to Python?
- Can you start by explaining what probabilistic programming is?
- What is the PyMC3 project and how did you get involved with it?
- The opening line for the project README is packed with a slew of terms that are rather opaque to the lay-person. Can you unpack that a bit and discuss some of the ways that PyMC3 is used in real-world projects?
- How much knowledge of statistical modeling and Bayesian statistics is necessary to make effective use of PyMC3?
- Can you talk through an example use case for PyMC3 to illustrate how you would use it in a project?
- How does it compare to the way that you would approach the same problem in a deterministic or frequentist modeling framework?
- Can you describe how PyMC3 is implemented?
- There are a number of other projects that build on top of PyMC3, what are some that you find particularly interesting or noteworthy?
- What do you find to be the most useful features of PyMC3 and what are some areas that you would like to see it improved?
- What have been the most interesting/unexpected/challenging lessons that you have learned in the process of building and maintaining PyMC3?
- What is in store for the future of PyMC3?
Keep In Touch
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