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So I am trying to remember how Bayesian updating works and reading the Wikipedia page on conjugate priors. I'm reading a lot about how either variance or the mean must be known. In what cases of real life data does this happen?

For example, lets say I have a basketball team like the Toronto Raptors and I believe that their points scored in the upcoming NBA season will come from some undetermined Normal Distribution. In the meantime, I want to start with the realized mean and variance from last years results and iteratively update my new distribution as the results come in. How would I go about doing this? Please let me know if there is something obvious I am missing or a better way to go about this.

EDIT

I have read the post that I am being marked a duplicate of:

I am trying to understand how to derive the posterior distribution of a parameter $\mu$ given data vector $z$, $P(\mu|z)$, where $$\mu \sim N(0,A)$$ and $$ z|\mu \sim N(\mu,1).$$

  1. I don't understand how it applies to what I asked.
  2. I am struggling to understand the 2nd assumption and how it relates to what I am asking. Maybe this is obvious to the people who marked it as a duplicate, but I am asking on a more basic level / asking for where I can go to learn about what I originally asked.
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