I have a complex problem that I can simplify into what seems like an application of Bayes theorem. The information I do have is something like this:
I know it rains 60% of the time in this location (P(A)).
I know it rains 70% of the time if it is June (P(A | B)).
I know it rains 72% of the time if it is a Wednesday (P(A | C)).
What is the probability of rain on a Wednesday in June? Is that written the same as P(A | B ∩ C)? And using Bayes, this translates to:
P (A | B ∩ C) = P (A ∩ B ∩ C) / P (B ∩ C)
And basically from what I have read on this subject, I cannot really know this answer for sure because I dont know P(A ∩ B ∩ C). If I cannot answer this for sure, can I make a pretty close approximation by just averaging P(A | B) and P(A | C)?
I am referencing Conditional probability of event A given events B and C occur? Events B and C are independent from each other and How can I calculate the conditional probability of several events?
What I have so far is to make an average of the two measurements. So if I have sample sizes as follows:
P(A): 300
P(A | B): 25
P(A | C): 30
Then a good approximation would be P (A | B ∩ C) = (25*0.7+30*0.72)/(25+30)=0.71
Does this sound reasonable?