Transcription of Topic 15: Maximum Likelihood Estimation
{{id}} {{{paragraph}}}
Topic 15: Maximum Likelihood Estimation . November 1 and 3, 2011. 1 Introduction The principle of Maximum Likelihood is relatively straightforward. As before, we begin with a sample X =. (X1 , .. , Xn ) of random variables chosen according to one of a family of probabilities P . In addition, f (x| ), x = (x1 , .. , xn ) will be used to denote the density function for the data when is the true state of nature. Then, the principle of Maximum Likelihood yields a choice of the estimator as the value for the parameter that makes the observed data most probable.
maximum can be a major computational challenge. This class of estimators has an important property. If ^(x) is a maximum likelihood estimate for , then g( ^(x)) is a maximum likelihood estimate for g( ). For example, if is a parameter for the variance and ^ is the maximum likelihood estimator, then p
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
{{id}} {{{paragraph}}}