Bayesian Analysis with Python

Learn the fundamentals of Bayesian modeling using Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries.

In this session, learn about the following things:
1. How do I appropriately specify a Bayesian model for my specific problem, and what considerations should I keep in mind?
2. What strategies do you recommend for selecting and tuning priors in Bayesian models? How can I ensure my priors are informative yet not overly influential?
3. How can I improve the computational efficiency of my Bayesian analysis, especially for larger datasets or complex models? Are there parallelization strategies or optimizations I should be aware of?
4. When working with multiple models, what methods or metrics should I use for model comparison in Bayesian analysis? How do I choose between different model specifications?
5. How should I handle missing data in Bayesian models? Are there specific imputation techniques or considerations unique to Bayesian analysis?

During the live event, you can ask more questions! We will try to get all the questions answered live, during the event.

We will also giveaway Ecopies to 2 lucky winners during the session. Comment with #packt to enter the raffle.