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MLE of the Poisson parameter, N %&’, is the unbiased estimate of the mean, $J(sample mean) More @Wikipedia
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- 20: Maximum Likelihood Estimation - Stanford UniversityMLE of the Poisson parameter, N %&’, is the unbiased estimate of the mean, $J(sample mean)
- Maximum likelihood estimation - WikipediaIn statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable.
- 1.2 - Maximum Likelihood Estimation | STAT 415 - Statistics OnlineSo, that is, in a nutshell, the idea behind the method of maximum likelihood estimation. But how would we implement the method in practice? Well, suppose we have a random sample X 1, X 2, ⋯, X n for which the probability density (or mass) function of each X i is f (x i; θ).
- Topic 15: Maximum Likelihood Estimation - The Department of MathematicsThe maximum likelihood estimator (MLE), ^(x) = argmax L( jx): (2) We will learn that especially for large samples, the maximum likelihood estimators have many desirable properties. However, especially for high dimensional data, the likelihood can have many local maxima. Thus, finding the global maximum can be a major computational challenge.
- Probability concepts explained: Maximum likelihood estimationA beginners introduction to the maximum likelihood method for parameter estimation (mle). It explains the method and goes through a simple example to demonstrate.
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