yeet991only
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- TL;DR Summary
- How to assign values to probabilities when there is no sample space? Is this allowed? Or it will seem like weight when you have a neural network?
Suppose this example:
You said a joke to a girl. She didn't laugh (let this be behaviour/evidence E). Was this because she is mad at you (let it be M) or because she didn't get the joke (let it be NJ , and it be 1 - p(M) = p(NJ)). So we have just 2 hypotheses M and NJ that is all. Now how to assign probabilities to such expression:
Suppose she was mad 2 times before this year.
So p(m) = 2 * average_days_mad/365 ??
I dont have any idea how to assign such probabilities..
How would NJ be assigned then?
Or should they be assigned as a grade out of 10?
Like from 0 to 1 , how likely do I think that M = "Mad" is true.
Like a weight from a neural network?
You said a joke to a girl. She didn't laugh (let this be behaviour/evidence E). Was this because she is mad at you (let it be M) or because she didn't get the joke (let it be NJ , and it be 1 - p(M) = p(NJ)). So we have just 2 hypotheses M and NJ that is all. Now how to assign probabilities to such expression:
Suppose she was mad 2 times before this year.
So p(m) = 2 * average_days_mad/365 ??
I dont have any idea how to assign such probabilities..
How would NJ be assigned then?
Or should they be assigned as a grade out of 10?
Like from 0 to 1 , how likely do I think that M = "Mad" is true.
Like a weight from a neural network?