#113 A Deep Dive into Bayesian Stats, with Alex Andorra, ft. the Super Data Science Podcast

Learning Bayesian Statistics

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

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

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Bayesian statistics is a powerful framework for handling complex problems, making use of prior knowledge, and excelling with limited data.
• Bayesian statistics provides a framework for updating beliefs and making predictions based on prior knowledge and observed data.
• Bayesian methods allow for the explicit incorporation of prior assumptions, which can provide structure and improve the reliability of the analysis.
• There are several Bayesian frameworks available, such as PyMC, Stan, and Bambi, each with its own strengths and features.
• PyMC is a powerful library for Bayesian modeling that allows for flexible and efficient computation.
• For beginners, it is recommended to start with introductory courses or resources that provide a step-by-step approach to learning Bayesian statistics.
• PyTensor leverages GPU acceleration and complex graph optimizations to improve the performance and scalability of Bayesian models.
• ArviZ is a library for post-modeling workflows in Bayesian statistics, providing tools for model diagnostics and result visualization.
• Gaussian processes are versatile non-parametric models that can be used for spatial and temporal data analysis in Bayesian statistics.

Chapters:

00:00 Introduction to Bayesian Statistics

07:32 Advantages of Bayesian Methods

16:22 Incorporating Priors in Models

23:26 Modeling Causal Relationships

30:03 Introduction to PyMC, Stan, and Bambi

34:30 Choosing the Right Bayesian Framework

39:20 Getting Started with Bayesian Statistics

44:39 Understanding Bayesian Statistics and PyMC

49:01 Leveraging PyTensor for Improved Performance and Scalability

01:02:37 Exploring Post-Modeling Workflows with ArviZ

01:08:30 The Power of Gaussian Processes in Bayesian Modeling

Thank you to my Patrons for making this episode possible!

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