You are assuming that the observation has no error margin.
Lets suppose that the priors are 51%A and 49%B
and then your new observation says “55%A and 45%B”
So—automatically you’d round your A-value up a little right?
but very few observations are going to be 100% accurate.
Lets say this one has an error rate of 10% so actually it could be only 50%A and 50%B, but has given you a false positive of 55%A
Are you better off? or have you just introduced more error into your estimations?
You are assuming that the observation has no error margin.
The observation is here defined by its effect on one’s probability distribution over utilities of outcomes. In this sense, the possibility of observational error is already included.
You are assuming that the observation has no error margin.
Lets suppose that the priors are 51%A and 49%B and then your new observation says “55%A and 45%B” So—automatically you’d round your A-value up a little right?
but very few observations are going to be 100% accurate. Lets say this one has an error rate of 10% so actually it could be only 50%A and 50%B, but has given you a false positive of 55%A
Are you better off? or have you just introduced more error into your estimations?
The observation is here defined by its effect on one’s probability distribution over utilities of outcomes. In this sense, the possibility of observational error is already included.
Ok—then I don’t understand it well enough.