Hi,

I'm interested to see if my solution is even remotely correct, the question is to derive the error formula given the number of experts/hypotheses (M) and also the error of each hypotheses (e) - assuming all to be the same, and that all are independent of one another...

I treated this as a probability problem (and unsure if I should have done so), and hence derived a formula which yields the following results...

M:5, e:0.10, 0.0100

M:5, e:0.20, 0.0800

M:5, e:0.40, 0.6400

M:10, e:0.10, 0.0002

M:10, e:0.20, 0.0134

M:10, e:0.40, 0.8602

M:20, e:0.10, 0.0000

M:20, e:0.20, 0.0034

M:20, e:0.40, 7.0448

The third column is the total error for the network I calculated; it certainly looks wrong from looking at the last value, but I'd like to know how to go about solving this as it's rather intriguing.

My `formula' in Python form is as follows...

def fact(k):

return reduce(lambda i, j : i*j, range(1, k+1))

for M in 5, 10, 20:

for e in 0.1, 0.2, 0.4:

m = M/2+1

_m = M-m

print "M:%d, e:%0.2f, %0.4f"%(M, e, (fact(M) * pow(e, m))/(fact(_m) * fact(m)))

Thanks to anyone who has a crack at solving this =)

Nima