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In 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. More @Wikipedia
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- 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.
- 20: Maximum Likelihood Estimation - Stanford UniversityMLE of the Poisson parameter, N %&’, is the unbiased estimate of the mean, $J(sample mean)
- 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.
- 1.2 - Maximum Likelihood Estimation | STAT 415 - Statistics OnlineMaximum likelihood estimates. Definition. Let X 1, X 2, ⋯, X n be a random sample from a distribution that depends on one or more unknown parameters θ 1, θ 2, ⋯, θ m with probability density (or mass) function f (x i; θ 1, θ 2, ⋯, θ m). Suppose that (θ 1, θ 2, ⋯, θ m) is restricted to a given parameter space Ω. Then:
- Maximum Likelihood Estimation (MLE) - BrilliantMaximum likelihood estimation (MLE) is a technique used for estimating the parameters of a given distribution, using some observed data.
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