Posted: Dec 24, 2011 11:14 am
LucidFlight wrote:Regina wrote:PR(H | E & B ) = [Pr(E | H & B) x Pr(H | B)] / Pr(E | B),
where Pr(E | B ) = Pr(E | H & B) x Pr(H | B) + Pr(E | ~H & B) x Pr(~H | B)= 42
However, given a posterior distribution P(θ|y) for quantity z of fried chicken thought to have been consumed, there is a estimated 100% chance of KFC resulting in a number 2.
Did you factor the strength of the posterior tensor into your flow calculations?
