
Probabilistic Reasoning in Intelligent Systems (1988) [pdf] - mindcrime
http://www.cogsci.northwestern.edu/Bayes/Pearl_1988.pdf
======
mindcrime
This is an older document, but has what appears to be some pretty foundational
material in it. The author, Judea Pearl[1], also wrote a famous book titled
_Causality_ [2].

[1]: [http://bayes.cs.ucla.edu/home.htm](http://bayes.cs.ucla.edu/home.htm)

[2]: [http://bayes.cs.ucla.edu/BOOK-2K/](http://bayes.cs.ucla.edu/BOOK-2K/)

------
herdrick
I like my tech and math with history. Knowing where the ideas came from and
what previous, wrong ideas they had to beat out is valuable in understanding
them. (Plus it reminds you that maybe your new ideas could be the next thing.)
But the full book (this is an excerpt) is more caught up in its time that I
would have liked - a lot of it seems to be arguing against critics. You
probably don't really need such an exhaustive treatment.

In contrast, Norvig's AIMA book has a chapter on the subject (bayes nets)
which is confident and compact. Start there.

Still I like this book and have recommended it.

------
darkxanthos
The ideas contained in this book and Causality have been a huge influence in
my decision of what to focus on learning as a data scientist. Very much
recommend it even if you feel like the book might be too dense for you. It
definitely was for me, but I still got a lot out of it.

~~~
ced
Out of curiosity, where did you go (what did you read) after these two books?

~~~
darkxanthos
I took a step back and read Think Bayes by Downey and watched some of his
youtube videos. Then Introduction to Bayesian Statistics by Bolstad is great
once you're reading to deal with probabilities. Now I'm reading

* Building probabilistic graphical models with Python (Karkera)

* Mastering probabilistic graphical models using Python (Ankan)

* Probabilistic Graphical Models Principles and techniques (Koller)

Having skimmed (just started reading them) I'm very excited to continue to
delve into them.

~~~
ced
Ah, I took a similar path - applied Bayes. I was wondering where to go next
for theory after Causality. It was such an interesting book.

~~~
darkxanthos
Probabilistic Graphical Models is a pretty good blend. Not sure how
theoretical you want.

------
sampo
The pdf seems to go up to the book's page 75. But (according to Amazon) to
book has 552 pages.

~~~
mindcrime
Yeah, I assume that was intentional, that this is just an excerpt. If the
whole book is legally available for free, I'm not sure where.

