First I want to thank you if you pay attention to my post. I apologize if it seems elementary to you, note that I searched a lot an answer before posting.
I have a particular informational framework where there are 3 events denoted A, B, C.
A= This guy is cool.
$\overline{A}$= This guy is not cool
B= Someone is here
$\overline{B}$= No one is here
C= He tells me if he is cool
$\overline{C}$= He does not tell me if he is cool
P(A), P(B) and P(C) are the probabilities associated with these events.
I assume that if the person who can advise me is not here, she cannot tell me something.
What is the probability that this guy is cool given the fact that no one is telling me something?
I propose the following formula:
$P(A|\emptyset)=\frac{p(A\cap B \cap \overline{C})+p(A\cap\overline{B})}{P(\emptyset)}$
Where $P(\emptyset)=P(A\cap B \cap \overline{C})+P(\overline{A}\cap B \cap \overline{C})+p(\overline{B})$.
Knowing the Bayes rule the previous equation bothers me because it contains a kind of union between events on the numerator. Maybe this is because there is a kind of redundancy between event B and C. My question is, is it the correct formula to compute the conditional probability in this framework?
Thank you