Quadrat Analysis_RW_Thomas

However it is always true that maximum likelihood

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Unformatted text preview: and for this reason the method is preferred to moments estimation. The main problem in using maximum likelihood estimation is that finding the parameter value which maximises (46) is usually a complex procedure both algebraitally and arithmetically. The solution is simple only for the Poisson And binomial distributions because in these cases the only parameter to be The fundamental principle in maximum likelihood estimation is that we obtain estimates of the model parameters such that the observed frequency distribution is predicted as closely as is possible by the corresponding model probabilities. Mathematically this procedure may be defined as finding that particular value of the model parameter y which maximises the value of the following likelihood function: mate of the mean is identical to the moments estimate. However, if other model parameters need to be estimated from the data the maximum likelihood procedures are awkward. For instance, in order to obtain the maximum likelihood estimate of the negative binomial k parameter...
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This note was uploaded on 02/15/2012 for the course GEO 6938 taught by Professor Staff during the Summer '08 term at University of Florida.

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