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Maximum likelihood estimation

Maximum-likelihood estimator

For <independent and identically distributed random variables> $X_1,\ldots,X_n\sim N(\mu,\sigma^2)$, the <maximum-likelihood estimators> are
$$
\widehat\mu=\overline X,
\qquad
\widehat{\sigma}^2=\frac1n\sum_{i=1}^n(X_i-\overline X)^2.
$$
The first is <unbiased estimator>[unbiased], while $\mathbb E\widehat{\sigma}^2=(n-1)\sigma^2/n$.

Normal mean and variance maximum-likelihood estimators

Past exam of the mathematics course of the University of Cambridge / 2019 / ib / Paper 1 / 7H / a / Solution

Normal mean and variance maximum-likelihood estimators

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Normal mean and variance maximum-likelihood estimators

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