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Oct 11, 2018 at 14:16 comment added Sachar Rosen I think that might be my problem: The difference between conditional probability function and a conditional density function. I'll google and proceed from there. Any online resource that is recommended?
Oct 10, 2018 at 13:18 comment added Xi'an @SacharRosen: Can you state clearly if you are aware of the difference between a conditional probability function and a conditional density function? If not, this would explain for the confusion and indicate that some further training in probability basics is needed.
Oct 8, 2018 at 15:49 comment added Sachar Rosen But in my case B= μ,σ2 ... I think that is where my confusion lies.
Oct 8, 2018 at 15:43 comment added adityar With probabilities, you can condition on random variables. This means that, if X and Y are random variables, it makes sense to say $P_X(x|Y = y)$, i.e. probability of X being x given that Y was observed to be y. In your example, notice that "B" is exactly like Y; writing it out properly, we have: $$A = 1\;if\;"sick", 0\;if\;"not\;sick"$$ $$B = 1\;if\;"shot",\;0\;if\;"took\;a\;shot"$$ Then, your statement $P(sick | shot)$ is more suggestively written as: $$P("sick"|"shot") = P(A = 1 |B = 1 )$$
Oct 8, 2018 at 15:41 comment added Sachar Rosen I think I am starting to semi-understand what you are saying: if Y is the probability that "I am sick" and (μ,σ2) is some kind of distribution of of how many people took the flu shot...I am trying to find μ and/or σ2? Now what does a distribution of "how many people took the flu shot" (My wording) means?
Oct 8, 2018 at 15:32 comment added Sachar Rosen I lost the understanding when you said "Here's where the posterior distribution comes in...". What allows us to say that the distribution (μ,σ2) can be treated as the "B" in p(A|B)? Can a small example be provided? Thanks!!!
Oct 8, 2018 at 15:11 history answered adityar CC BY-SA 4.0