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1 year, 4 months ago
This question already has answers here:
I am struggling with the
Wikipedia entry on Likelihood.
In an example it mentions
$L(P_H = 0.5 |HH) = 0.25$
It mentions that
Bayes' theorem implies that the posterior probability is proportional
to the likelihood times the prior probability.
I am trying to understand, in our scenario, what the prior and the post should be. I thought of
post = prior * likelihood = 0.5 * 0.25 = 0.125
This seems way too small. How is the "proportional" calculated?
Jan 25, 2022 at 3:07
639 3 3 silver badges 18 18 bronze badges
When you multiply a prior probability distribution by the likelihood function you can get a distribution that has an integral of more or less than one. It is not a probability distribution until you scale it to get that integral back to one.
Jan 25, 2022 at 6:31
Michael Lew Michael Lew
12.2k 2 2 gold badges 33 33 silver badges 49 49 bronze badges
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