Probability Theory 30 | Strong Law of Large Numbers

[@Happy_Abe]

This is just like almost everywhere convergence implies convergence in measure for measurable functions in measure theory

[@andrewxiwu]

It feels that there might be something about notation. When we define that average random variable, we are creating a new sample space with products, so it is a product of \Omega, but then when we write that probability of \omega (small one), it is not in \Omega, but in \Omega^n now. Am I right?

[@brightsideofmaths]

You are right. I should be more careful explaining notations :) Maybe part 28 helps a little bit to understand the meaning of Omega?