maximum likelihood

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maximum likelihood

n
1. (Statistics) the probability of randomly drawing a given sample from a population maximized over the possible values of the population parameters
2. (Statistics) the non-Bayesian rule that, given an experimental observation, one should utilize as point estimates of parameters of a distribution those values which give the highest conditional probability to that observation, irrespective of the prior probability assigned to the parameters
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The method of maximum likelihood, as advocated by Fisher in his important papers [9, 10], has become one of the most significant tools for estimation and inference available to statisticians.
The estimation of the parameters of model was done by using method of Maximum Likelihood. This method deals with finding the estimates of the coefficients which maximize the probability of selecting a sample actually obtained (Kennedy, 2003).
This paper uses the method of maximum likelihood to estimate the parameters.
However, the operational risk literature reports that attempts to fit operational losses using truncated severity distributions by the method of maximum likelihood estimation are not always successful.
By knowing the density function of this new process we can find discretized likelihood function and use the conventional method of maximum likelihood estimation to find a closed formula for the parameters.
In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without believing in Bayesian probability or using any Bayesian methods.

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