Suppose we are trying to measure the true mean
of some quantity. We make repeated measurements
Intuitively we say the true value of the mean
is likely to be close to the mean of our measurements,![]()
The maximum likelihood method is a general method for estimating parameters of interest from data.
1. Assume we have made
measurements of![]()
2. Assume we know the probability distribution function that describes
where a is the parameter who value we want to estimate.
3. The probability of measuring
is
the probability of measuring
is
the probability of measuring
is![]()
4. If the measurements are independent, the probability of getting the measurements
is![]()
5. We want to maximise
and solve for
We may do this by differentiation. The value of
that gives the maximum for
also gives the maximum for
For ease of calculation we may take logs and convert the product into a sum. Either way we solve
for![]()
Example: Let
be given by a Gaussian distribution., let
be the mean of the Gaussian. We want the best estimate of
labelled
from our set of
measurements![]()
so![]()
Taking natural logs gives
and differentiating this gives (notice the first term vanishes because it contains no occurrences of
)
![]()