释义 |
Maximum LikelihoodThe procedure of finding the value of one or more parameters for a given statistic which makes the knownLikelihood distribution a Maximum. The maximum likelihood estimate for a parameter is denoted.
For a Bernoulli Distribution,
| (1) |
so maximum likelihood occurs for . If is not known ahead of time, the likelihood function is
where or 1, and , ..., .
| (3) |
| (4) |
| (5) |
| (6) |
For a Gaussian Distribution,
| (7) |
| (8) |
| (9) |
gives
| (10) |
| (11) |
gives
| (12) |
Note that in this case, the maximum likelihood Standard Deviation is the sample Standard Deviation, whichis a Biased Estimator for the population Standard Deviation.
For a weighted Gaussian Distribution,
| (13) |
| (14) |
| (15) |
gives
| (16) |
The Variance of the Mean is then
| (17) |
But
| (18) |
so
For a Poisson Distribution,
| (20) |
| (21) |
| (22) |
| (23) |
See also Bayesian Analysis References
Press, W. H.; Flannery, B. P.; Teukolsky, S. A.; and Vetterling, W. T. ``Least Squares as a Maximum Likelihood Estimator.'' §15.1 in Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd ed. Cambridge, England: Cambridge University Press, pp. 651-655, 1992.
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