Thank you so much for your hard work! I'm a statistics BA student and wasn't given the opportunity to learn any measure theory course, and now I'm closing the gap, keep going!
Hi Dr Corcoran. When would you post Lecture 3,4,5 etc in this series? I'm too excited for measure theoretic probability and can't wait to learn from your videos.
Hi! Thank you for your kind words. I had some things come up that took my attention away for the last two weeks but I will have videos coming out regularly again before the end of this week. Thanks!
Wow, fantastic. I’ve been trying to learn this material on my own (University was long, long ago), but never got very far. I love your style. Thanks for making this available. One thing that has always bothered me was understanding the need for measure-theoretic probability given much of probability and mathematical statistics seem to have developed without the formalism, or prior to its appearance.. I find the history behind all of this just as fascinating.
Me too. I think that combining the treatment of discrete and continuous distributions is not all that compelling. However, I think measure theory is quite useful for the study of stochastic processes where you sometimes need to talk about random functions. Suppose you want to choose a random function from some space of functions. You can't describe this sort of thing with a pdf! (p.s. Thanks for watching!)
@@AProbabilitySpace Choosing a random function from a space of functions is a great example to explain why measure-theoretic probability is needed. I whole heartedly agree that combining the treatment of discrete and continuous distributions is not compelling enough. You are an outstanding teacher. A true outlier.
Hi Professor, I bought the book "A first look at rigorous probability theory" by Rosenthal as you suggested. Can you also please tell me should I also buy the First Look At Stochastic Processes by Rosenthal?
Why does probability always assume increasing likelihood? Since unions can go to infinity in a negative direction but say also it goes positive as well but converges to a finite number after a very long but not infinite frame. Certainly that number is negative
Thank you so much for your hard work! I'm a statistics BA student and wasn't given the opportunity to learn any measure theory course, and now I'm closing the gap, keep going!
Incredible videos. Your students are lucky to have you :)
Thank you!
👍
Hi Dr Corcoran. When would you post Lecture 3,4,5 etc in this series? I'm too excited for measure theoretic probability and can't wait to learn from your videos.
Hi! Thank you for your kind words. I had some things come up that took my attention away for the last two weeks but I will have videos coming out regularly again before the end of this week. Thanks!
Wow, fantastic. I’ve been trying to learn this material on my own (University was long, long ago), but never got very far. I love your style. Thanks for making this available. One thing that has always bothered me was understanding the need for measure-theoretic probability given much of probability and mathematical statistics seem to have developed without the formalism, or prior to its appearance.. I find the history behind all of this just as fascinating.
Me too. I think that combining the treatment of discrete and continuous distributions is not all that compelling. However, I think measure theory is quite useful for the study of stochastic processes where you sometimes need to talk about random functions. Suppose you want to choose a random function from some space of functions. You can't describe this sort of thing with a pdf! (p.s. Thanks for watching!)
@@AProbabilitySpace Choosing a random function from a space of functions is a great example to explain why measure-theoretic probability is needed. I whole heartedly agree that combining the treatment of discrete and continuous distributions is not compelling enough. You are an outstanding teacher. A true outlier.
Hi Professor, I bought the book "A first look at rigorous probability theory" by Rosenthal as you suggested. Can you also please tell me should I also buy the First Look At Stochastic Processes by Rosenthal?
Why does probability always assume increasing likelihood? Since unions can go to infinity in a negative direction but say also it goes positive as well but converges to a finite number after a very long but not infinite frame. Certainly that number is negative
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