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Echoing other comments here, this seems like a hard way to start learning probability. It sounds like the goal is to make probability easier to understand based

Echoing other comments here, this seems like a hard way to start learning probability. It sounds like the goal is to make probability easier to understand based on what you say here (https://betanalpha.github.io/writing https://betanalpha.github.io/writing) > In this case study I attempt to untangle this pedagogical knot to illuminate the basic concepts and manipulations of probability theory and how they can be implemented in practice But I think this is too hard. I really loved "Probability For The Enthusiastic Beginner" http://a.co/2kp5PZd http://a.co/2kp5PZd

gh02t 9y ago
"For Scientists and Engineers" sounds to me like it's targeting people who already have a strong background in more advanced mathematics, but not necessarily probability and measure theory. If so I think this is a decent way to go about it. I'm an engineer and sometimes mathematician that works with fairly in-depth probability theory related things and this looks to be a condensed version of a lot of the basic stuff I had to self learn when I was getting into what I work on now. I'm in a niche area though, and I do wonder if this really is that useful to most scientists and engineers.
- soVeryTired 9y ago
  Reading 'All of statistics' by Wasserman would be a much better bet than diving deep into measure-theoretic probability though.
  - gh02t 9y ago
    Depends on your goal, statistics and probability theory are separate (though of course related) fields with different applications. For me I really needed the measure-theoretic bits because I was (am) working on modeling ergodic processes. This article honestly doesn't go into enough detail to be especially useful but I like the direction the author approaches it from. I'm familiar with the text you mentioned, it's certainly good and would be better than this article for most, but comparing a textbook to a short web article isn't exactly fair.

gh02t 9y ago

"For Scientists and Engineers" sounds to me like it's targeting people who already have a strong background in more advanced mathematics, but not necessarily probability and measure theory. If so I think this is a decent way to go about it. I'm an engineer and sometimes mathematician that works with fairly in-depth probability theory related things and this looks to be a condensed version of a lot of the basic stuff I had to self learn when I was getting into what I work on now. I'm in a niche area though, and I do wonder if this really is that useful to most scientists and engineers.

soVeryTired 9y ago
Reading 'All of statistics' by Wasserman would be a much better bet than diving deep into measure-theoretic probability though.
- gh02t 9y ago
  Depends on your goal, statistics and probability theory are separate (though of course related) fields with different applications. For me I really needed the measure-theoretic bits because I was (am) working on modeling ergodic processes. This article honestly doesn't go into enough detail to be especially useful but I like the direction the author approaches it from. I'm familiar with the text you mentioned, it's certainly good and would be better than this article for most, but comparing a textbook to a short web article isn't exactly fair.