Osvaldo Martin writes:

The third edition of Bayesian Analysis with Python serves as an introduction to the basic concepts of applied Bayesian modeling. It adopts a hands-on approach, guiding you through the process of building, exploring and expanding models using PyMC and ArviZ. The field of probabilistic programming is in a different place today than it was when the first edition was devised in the middle of the last decade. The journey from its first publication to this current edition mirrors the evolution of Bayesian modeling itself – a path marked by significant advancements, growing community involvement, and an increasing presence in both academia and industry. Consequently, this updated edition also includes coverage of additional topics and libraries such as Bambi, for flexible and easy hierarchical linear modeling, PyMC-BART, for flexible non-parametric regression; PreliZ, for prior elicitation; and Kulprit, for variable selection.

Whether you’re a student, data scientist, researcher, or developer aiming to initiate Bayesian data analysis and delve into probabilistic programming, this book provides an excellent starting point. The content is introductory, requiring little to none prior statistical knowledge, although familiarity with Python and scientific libraries like NumPy is advisable.

By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You’ll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.

See more at the book website

Osvaldo spent one year at Aalto in Finland (unfortunately, during the pandemic) so I know he knows what he writes. Bambi is rstanarm / brms style interface for building models with PyMC in Python ecosystem, and Kulprit is the Python version of projpred (in R) for projective predictive model selection (which is one of my favorite research topics).