Bayesian Methods and Applications, with Alexandre Andorra

Is he a man or a country? Find out in today's episode with Alexandre Andorra — developer of the leading Bayesian library for Python, implementer of commercial Bayesian models and leading Bayesian educator/podcaster!

More on Alex:

• Co-Founder and Principal Data Scientist at PyMC Labs, a firm that develops PyMC (the leading Python library for Bayesian statistics) and consults with their clients to implement profit-increasing Bayesian models.

• Co-Founder and Instructor at an online learning platform called Intuitive Bayes that provides free Bayesian stats education.

• Creator and Host of an excellent podcast called Learning Bayesian Statistics.

Today’s episode will probably appeal most to hands-on practitioners like statisticians, data scientists and machine learning engineers, but the episode also serves as an introduction to Bayesian statistics for anyone who’d like to learn about this important, unique and powerful field.

In today’s episode, Alex details:

• What Bayesian statistics is.

• The situations where Bayesian stats can solve problems that no other approach can.• Resources for learning Bayesian stats.

• The key Python libraries for implementing Bayesian models yourself.

• How Gaussian Processes can be incorporated into a Bayesian framework in order to allow for especially advanced and flexible models.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

PyMC for Bayesian Statistics in Python

Learn how Bayesian Statistics can be more powerful and interpretable than any other data modeling approach from Dr. Thomas Wiecki, a Core Developer of PyMC — the leading Bayesian software library for Python.

Thomas:
• Has been a Core Developer of PyMC for over eight years.
• Is Co-Founder and CEO of PyMC Labs, which solves commercial problems with Bayesian data models.
• Previously, he worked as VP Data Science at Quantopian Inc.
• Holds a PhD in Computational Neuroscience from Brown University.

Today’s episode is more on the technical side so will appeal primarily to practicing data scientists.

In this episode, Thomas details:
• What Bayesian statistics is.
• Why Bayesian statistics can be more powerful and interpretable than any other data modeling approach.
• How PyMC was developed and how it trains models so efficiently.
• Case studies of large-scale Bayesian stats applied commercially.
• The extra flexibility of *hierarchical* Bayesian models.
• His top resources for learning Bayesian stats yourself.
• How to build a successful company culture.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.