It's been a month and I can't understand anything. What's the real difference between fields and sigma-fields? Why do we even want do define everything on them? What's the relationship between Borel sets and real numbers? What the hell did Radon and his pal really prove?
I just don't get anything. For Christ's sake, I was a business studies major.
Don't just read the chapter like a novel and conversely don't just pound on the problems without looking back at the material in the chapter.
It should be sort of iterative between problem set and chapter. You are also going to have to increase the time you spend on this. Instead of going out 2 nights a week, go only 1 or 0. Be an adult.
Axler's new book is perhaps the clearest and easiest to learn from measure theory book written to day, in my opinion. And, the electronic version is free. So take a look:
It's been a month and I can't understand anything. What's the real difference between fields and sigma-fields? Why do we even want do define everything on them? What's the relationship between Borel sets and real numbers? What the hell did Radon and his pal really prove?
I just don't get anything. For Christ's sake, I was a business studies major.
1. I’ve heard good things about this book https://www.amazon.com/Theoretic-Probability-Statistical-Probabilistic-Mathematics-ebook/dp/B00INYGDTW
2. Shreve 2 was my first intro to MT as a finance quant, or introduces MT with an eye on stoch calc models in finance. Written for a relatively unsophisticated audience.
That is a very good book, but it is also not an easy book, and nowhere near the "measure theory for t-tard's" book that OP is seeking.
1. I’ve heard good things about this book https://www.amazon.com/Theoretic-Probability-Statistical-Probabilistic-Mathematics-ebook/dp/B00INYGDTW
2. Shreve 2 was my first intro to MT as a finance quant, or introduces MT with an eye on stoch calc models in finance. Written for a relatively unsophisticated audience.
Also, on the same page as the link below, Axler provides a freely available review of all the the undergraduate real analysis you need to read his book.
That too, is a very well written source. So download that to have at your side as you read his measure theory book.
Axler's new book is perhaps the clearest and easiest to learn from measure theory book written to day, in my opinion. And, the electronic version is free. So take a look:
https://measure.axler.net/It's been a month and I can't understand anything. What's the real difference between fields and sigma-fields? Why do we even want do define everything on them? What's the relationship between Borel sets and real numbers? What the hell did Radon and his pal really prove?
I just don't get anything. For Christ's sake, I was a business studies major.
That is a very good book, but it is also not an easy book, and nowhere near the "measure theory for t-tard's" book that OP is seeking.
1. I’ve heard good things about this book https://www.amazon.com/Theoretic-Probability-Statistical-Probabilistic-Mathematics-ebook/dp/B00INYGDTW
2. Shreve 2 was my first intro to MT as a finance quant, or introduces MT with an eye on stoch calc models in finance. Written for a relatively unsophisticated audience.
That’s a pity, not what I gather from the Amazon blurb or r reviews.
A chatty style(Pollard) or focus on applications(Shreve) should help. OP has a tough task going from “tard” to MT, motivation is of the essence.
That is a very good book, but it is also not an easy book, and nowhere near the "measure theory for t-tard's" book that OP is seeking.1. I’ve heard good things about this book https://www.amazon.com/Theoretic-Probability-Statistical-Probabilistic-Mathematics-ebook/dp/B00INYGDTW
2. Shreve 2 was my first intro to MT as a finance quant, or introduces MT with an eye on stoch calc models in finance. Written for a relatively unsophisticated audience.
That’s a pity, not what I gather from the Amazon blurb or r reviews.
A chatty style(Pollard) or focus on applications(Shreve) should help. OP has a tough task going from “tard” to MT, motivation is of the essence.
I would appreciate some motivation tbh. So far it's like a highly generalized version of statistics, but I just don't see why should we bother with starting from axioms, measure spaces, set functions etc instead of taking an undergrad approach.
Does it allow us to work with cases where simpler methods fail? No clue. For example, when do we need to prove that the expectations fail to exist? No clue. There are no examples in our notes.
That is a very good book, but it is also not an easy book, and nowhere near the "measure theory for t-tard's" book that OP is seeking.1. I’ve heard good things about this book https://www.amazon.com/Theoretic-Probability-Statistical-Probabilistic-Mathematics-ebook/dp/B00INYGDTW
2. Shreve 2 was my first intro to MT as a finance quant, or introduces MT with an eye on stoch calc models in finance. Written for a relatively unsophisticated audience.
That’s a pity, not what I gather from the Amazon blurb or r reviews.
A chatty style(Pollard) or focus on applications(Shreve) should help. OP has a tough task going from “tard” to MT, motivation is of the essence.I would appreciate some motivation tbh. So far it's like a highly generalized version of statistics, but I just don't see why should we bother with starting from axioms, measure spaces, set functions etc instead of taking an undergrad approach.
Does it allow us to work with cases where simpler methods fail? No clue. For example, when do we need to prove that the expectations fail to exist? No clue. There are no examples in our notes.
If you're intention is for econometrics research, it's necessary to understand what is a stochastic process, what is the expectations and conditional expectations operator, what is a martingale, all the implications thereof.