Best products from r/probabilitytheory
We found 11 comments on r/probabilitytheory discussing the most recommended products. We ran sentiment analysis on each of these comments to determine how redditors feel about different products. We found 10 products and ranked them based on the amount of positive reactions they received. Here are the top 20.
1. Probability Theory: The Logic of Science
- Used Book in Good Condition
Features:
2. General Irreducible Markov Chains (Cambridge Tracts in Mathematics)
- Used Book in Good Condition
Features:
3. Introduction to Probability (Chapman & Hall/CRC Texts in Statistical Science)
- CRC Press
Features:
4. Introduction to Probability, 2nd Edition
- Brand New Textbook
- U.S Edition
- Fast shipping
Features:
5. An Introduction to Bayesian Analysis: Theory and Methods (Springer Texts in Statistics)
- Intel Core i5 3337U 1.8 GHz
- 8 GB SO-DIMM
- 750 GB 5400 rpm Hard Drive
- 15.6-Inch Screen
- Windows 8
Features:
6. The Art Of Probability
- Product Type - Adapter
- Warranty - Lifetime
- Compatible with x4, x8, and x16 full-height PCI Express slots
- Support for most network operating systems (NOS)
Features:
7. Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
MIT Press MA
8. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Mit Press
By old-fashioned definitions. See General Irreducible Markov Chains and Non-Negative Operators by Nummelin or Markov Chains and Stochastic Stability by Meyn and Tweedie.
Markov chains used to be defined the way you say, because there were no good methods to handle general state spaces. Now there are, so the old-fashioned definition leaves out the most interesting applications for no good reason.
I really loved Harvard Stat 110 (both the book and the videos for the lectures)
http://www.amazon.com/Introduction-Probability-Edition-Dimitri-Bertsekas/dp/188652923X/ref=sr_1_1?ie=UTF8&qid=1394424420&sr=8-1&keywords=bertsekas+probability
You can find the video lectures from http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/ or taking the course on edX https://www.edx.org/course/mitx/mitx-6-041x-introduction-probability-1296
*Solutions to the book exercises can be found on the book's website. Perfect for self-taught learner.
Jaynes' Probability Theory is fantastic.
Most (all?) rigorous treatments Bayesian methods require a rigorous foundation in probability theory - I think that is self explanatory.
The usual foundation for probability theory is measure theory. So, you can't have rigorous foundation in probability theory without knowing measure theory. There are other foundations, but the vast majority of the time we use measure theory - for example, convergence results like the SLLN use the measure theoretic concept of almost-sure convergence.
So, for instance, I could direct you to be a rigorous Bayesian book - for example, this book - but they will assume you already know things like the martingale convergence theorem, Radon-Nikodym, and Borel-Cantelli which are typically covered in measure-theoretic probability.
I started reading Hamming's book:
http://www.amazon.com/The-Art-Probability-Richard-Hamming/dp/0201406861
I like his style, but it's going to be coming at it from more of an engineering point of view rather than pure mathematics.
I'm only part way through it myself, but here's one I've been recomended in the past that I've been enjoying so far:
Probability Theory: The Logic of Science by E.T. Jaynes
http://www.amazon.com/Probability-Theory-The-Logic-Science/dp/0521592712
http://omega.albany.edu:8008/JaynesBook.html
The second link only appears to have the first three chapters in pdf (though it has everything as postscript files), but I would be shocked if you couldn't easilly find a free pdf off the whole thing online with a quick search.