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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