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