Measure theoretic probability book for self-study Background:
Heading into my final year of undergraduate and I'm taking a class on Measure-theoretic probability theory. I have taken introductory probability and statistics classes, as well as Real Analysis.
What I'm looking for: I learn way better from books than lectures so I'm looking for a self-study book. When I was taking Real Analysis I read Understanding Analysis by Stephen Abbott and I really loved it. So I'm looking for a text that is similar in style to this, i.e adequate for self-study, explains things well with diagrams, has a decent number of exercises (and preferably solutions) and is not simply a "reference text".
So far I've found:

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*Probability and Measure by Billingsley

*Measure, Integral and Probability by Capinski

*Probability Theory and Examples by Durrett

*Foundations of Modern Probability by Kallenberg

*Probability Theory: A Comprehensive Course by Klenke

*A User's Guide to Measure Theoretic Probability by Pollard

*Probability and Measure Theory by Ash

*A First Look at Rigorous Probability Theory by Rosenthal

*Measures, Integrals and Martingales by Schilling

*Real Analysis: Theory of Measure and Integration by Yeh

Obviously, a ton of choices here and I can't read all of them. Would appreciate any suggestions on the above texts or perhaps another one, thank you!
 A: There are a few classic books that you are missing:
Feller's An Introduction to Probability Theory and Its Applications. (vol. 1 & 2) are two excellent books to read. Volume 1 is particularly friendly while volume 2 is less so, yet still quite accesible.
Ash's Real Analysis and Probability (different from the one you mentioned) is my personal favourite. Ash works with analysis quite deeply before touching on probability. The payoff is that he does everything mathematically justifiedly and quite comprehensively.
Breiman's Probability is also a very good one. Strong analysis and intuition here.
Resnick's A probability path. Not my favourite but liked by many.
Casella and Berger's Statistical Inference. Has nice proofs and it is quite accessible and formal although its focus is on (theoretical) statistics.
John A. Rice's Mathematical Statistics and Data Analysis. Again, another statistical book but it is quite a nice complement to a probabilist repertoire.
Kay Lay Chung's A course in Probability Theory. Very famous, strong analysis, good ideas, quite dry and quite reading pedantic at times.
Jordan M. Stoyanov's Counter Examples in Probability. A very good book to develop probabilistic intuition.
