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Probability and Measure Lecture 1: What is a Measure? 

Adam Kashlak
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This channel is now deprecated. Please find this video on my new channel: • Probability and Measur...
In this video, we introduce some of the main definitions in Measure theory. This includes measures and sigma-fields and some examples to set the stage for future lectures.
Written Lecture Notes can be found at sites.ualberta...

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21 окт 2024

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Комментарии : 3   
@sallanmega1
@sallanmega1 2 года назад
Hi! I am a MS in Data Science student from Spain that just found this video series and I must say that I'm loving It. This is one of the most intuitive explanations of measure theory applied to probability that I have found on RU-vid. Thank you very much for sharing the course with everyone online!
@minhokang3016
@minhokang3016 2 года назад
I have a question at 45:50. I can't understand why v(A) is a uniform distribution. If A is a subset of omega(whole space), v(A) violates the condition of probability measure. Because the cardinality of a power set is 2 to the n, so if I sum all measures on a power set, It exceeds at least n.
@adamkashlak7501
@adamkashlak7501 2 года назад
In this case, nu(A) is a normalized counting measure. That is, it assigns to any A subset {1,2,...,n} the value |A|/n. Hence, if A = {1,...,n}, then nu(A) = 1. Here Omega is {1,...,n}. It sounds like you are trying to define a probability measure on the power set, i.e. some mu such that mu( 2^Omega )=1. In this case, we just want nu(Omega)=1. The power set, in this case, is the sigma-field of all 2^n measurable subsets of Omega.
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