Right up until your reply prompted me to write a program to check your argument, I wasn't thinking in terms of relative frequencies at all, but in terms of probability distributions.

I haven't learned the rules for relative frequencies yet (by which I mean thing like "(don't) include counts of variables that have a correlation of 1 in your denominator"), so I really have no idea.

Here is my program - which by the way agrees with neq1's comment here, insofar as the "magic trick" which will recover 1/2 as the answer consists of commenting out the TTW line.

However, this seems perfectly nonsensical when transposed to my spreadsheet: zeroing out the TTW cell at all means I end up with a total probability mass less than 1. So, I can't accept at the moment that neq1's suggestion accords with the laws of probability - I'd need to learn what changes to make to my table and why I should make them.

from random import shuffle, randint
flips=1000
HEADS=0
TAILS=1
# individual cells
HMW = HTW = HMD = HTD = 0.0
TMW = TTW = TMD = TTD = 0.0
def run_experiment():
global HMW, HTW, HMD, HTD, TMW, TTW, TMD, TTD
coin = randint(HEADS,TAILS)
if (coin == HEADS):
# wake Beauty on monday
HMW+=1
# drug Beauty on Tuesday
HTD+=1
if (coin == TAILS):
# wake Beauty on monday
TMW+=1
# wake Beauty on Tuesday too
TTW+=1
for i in range(flips):
run_experiment()
print "Total samples where heads divided by total samples ~P(H):",(HMW+HTW+HMD+HTD)/(HMW+HTW+HMD+HTD+TMW+TTW+TMD+TTD)
print "Total samples where woken F(W):",HMW+HTW+TMW+TTW
print "Total samples where woken and heads F(W&H):", HMW+HTW
print "P(W&H)=P(W)P(H|W), so P(H|W)=lim F(W&H)/F(W)"
print "Total samples where woken and heads divided by sample where woken F(H|W):", (HMW+HTW)/(HMW+HTW+TMW+TTW)