Suppose we are trying to measure the true meanof 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 mademeasurements of
2. Assume we know the probability distribution function that describeswhere a is the parameter who value we want to estimate.
3. The probability of measuringis
the probability of measuring
is
the probability of measuring
is
4. If the measurements are independent, the probability of getting the measurementsis
5. We want to maximiseand 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: Letbe 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 givesand differentiating this gives (notice the first term vanishes because it contains no occurrences of
)