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I would recommend incoming readers to really invest time on theory before starting this book (I know that is exactly what this book does not want you to do) and

I would recommend incoming readers to really invest time on theory before starting this book (I know that is exactly what this book does not want you to do) and use this book as the driver of your mental model the next time you encounter Bayesian methods. I personally find exceptional clarity once I see the code for a certain technique in Machine Learning. Often times, the theory skips certain implementation details which always leaves a void for me. After having read quite a bit on Bayesian Learning sometime ago, it is easy to connect this guide back to theory. That immediate click of ideas is rewarding!

Woberto 9y ago
Do you have any suggested reading for the theory? I'm going to be using QUESO[1] for some research with Bayesian statistics and am trying to learn more of the background knowledge. [1]: https://github.com/libqueso/queso https://github.com/libqueso/queso
- activatedgeek 9y ago
  I absolutely love David Barber's book (BRML). It is available here - http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... I'm inclined towards Machine Learning and hence the bias. Not sure if this would cover the statistics parts but I think at least the fundamentals are the same.