I am looking for a solution to a Bayesian algorithm problem that
concerns calculating additional weighting inputs. Bayes' theorem tells
me that I can have several conditional probabilities that are
dependent on the main branch or condition and but are also independent
of each other. That by separating them individually and then
collectively applying the formula to all the dependent probabilities,
I can take the output of one Bayesian probability and feed it to the
next, this acting as the prior value, until there is a final
probability that will be my answer. For instance calculating the
probability of whether a student passes a subject or not may depend on
whether the student did all class tests, submitted all coursework, did
all presentations etc, however each of these individual conditions may
have their own weighting value, related to their importance. How then
do I include this weighting as part of the prior value input for the
next probability? I have tried to cut and paste to give a diagrammatic
representation of what I mean, but it did not come out properly
ss1 happy
ss2 indifferent
S1 ss3 sad
sunny
A
rain
S0
Above ss1, ss2 and ss3 are independent of each other but each has and
additional weighting value in terms of importance.
.
.
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