|
- P(x|α) = e-α αx / x!,
integer x≥0,
- has one parameter, α, and
mean = variance = α.
- Note, P(x|α) = P(x-1|α) . α / x
- so P(x|α) increases with x
while x<α and decreases when x>α.
-
- Given n data, x1, x2, ..., xn,
the likelihood
- P(x1, x2, ..., xn | α)
= e-n.α.α∑xi
/ (x1! ... xn!)
-
- neg log likelihood,
- L = - log P(x1, x2, ..., xn | α)
- = n.α - (∑xi)logα
+ log x1! + ... + log xn!
- 1st derivative
- d L / d α
= n - (∑xi)/α
-
Equating this to zero,
αmaxLH = (∑xi) / n.
|
- 2nd derivative
- d2 L / d α2
= (∑xi) / α2
- which has expectation, i.e.,
Fisher information,
Fα = nα/α2 = n/α,
- note +log Fα = log n - log α
-
- Assume prior,
h α = (1/A).e-α/A,
which has mean A.
- Note - log(h α) = log A + α/A.
-
- Message length,
- m
= - log(h α)
+ L
+ 1/2 log Fα + (-log 12 +1)/2
-
- To estimate α,
differentiate m with respect to α
- d m / d α
= 1/A
+ n - (∑xi)/α
- 1/(2α)
- equate to zero
- αMML
= (∑xi + 1/2) / (n + 1/A)
- uncertainty region sqrt(12/FαMML)
= sqrt(12 αMML / n)
-- LA, 3/7/2007
- Some sanity checks:
- If it happens that x1 = ... = xn = x then
αMML -> x, and the
uncertainty region ->0 as n->∞.
- If x1 = ... = xn = 0 then
αMML->0 as n->∞.
- αMML -> αmaxLH = ∑xi/n
as n->∞.
See [IP 1.2] for an implementation.
|
|