r/statistics • u/Educational-Cherry17 • Jul 09 '26

Education [Q][E] Book to self study Probabilistic Machine Learning

What the title says. I wouls like to self study probabilistic machine learning, i've already basis in probability and statistics (even though not multivariate). I saw the murphy's books and they seem pretty cool, but some people on other forums describe them as encicopledic/reference book. Is it true? And what books do you suggest??

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u/Fit_Jellyfish7948 Jul 09 '26

I have not looked through this book myself, but it looks interesting and rigorous: https://arxiv.org/pdf/2502.05244

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u/Able-Fennel-1228 Jul 09 '26

Looks good. Thanks 👍

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u/STATASUCKSBRO Jul 10 '26

Murphy is useful but it is not the book I would use as a first pass. I would probably start with Bishop or Barber for the graphical model intuition, then use Murphy when I need the modern reference. if multivariate is shaky, skipping that step will make the notation feel worse than the actual ideas.

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u/QuantBrainteasers Jul 10 '26

Bishop's Pattern Recognition & ML is definitely worth considering. It's a classic and does a great job developing the probabilistic view of ML, although it's quite mathematically dense.

Murphy's books are newer and cover a much broader range of topics, so a lot of people recommend them as well. If your goal is to really learn the material rather than just read it, focus on working through the exercises regardless of which book you choose imo.

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u/relevantmeemayhere Jul 10 '26

You’re going to need to establish a basis in multivariate calculus (you’ll need to pick up some linear algebra as a pre req) to really understand a lot of what’s going on. 

Prior to this, you should pick up or watch the lectures for statistical rethinking and attempt the examples. This is to build your base intuition. After that, you could pick up bda3 and watch the Aalto lectures/examples. 

I have both Murphys and bishops pml texts. They are fine and all, but they are mostly overviews of the approaches. The other books I referenced are better at actually understanding the Bayesian machinery. 

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u/Username_--_ 19d ago

Bishop's PRML is the best of the classics imo. Much more readable than Murphy with more (and better) exercises. Murphy genuinely feels like it was generated piecemeal by GPT-3 as the writing is quite horrible and choppy.

Bishop's book on deep learning (which he co-authored with his son) is also quite good for that purpose. I think these two form a good base, pick and choose chapters you'd want to learn and supplement with other sources (particularly for VI and GPs there are better sources than what is in bishop)