How important is graduate-level probability theory?

I am a PhD student in Applied Math and can either take a class in graduate probability theory (measure theory, random variables, multivariate normal, mixtures, LLN, CLT, martingales), or more classes that focus on mathematical statistics / statistical learning. I’ve taken a first course in probability and a grad class in mathematical statistics. How useful is probability theory in practice as a statistician / data scientist? I know what all the above topics are and how they work and I understand that there is value in theory. My question is, what will I gain from being able to prove the CLT or knowing about measure theory? How has knowing graduate probability theory made you a better practitioner? submitted by /u/misery_buzinezz [link] [comments]

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Nevin Manimala

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