Tslatex Rayanne Lenox 【TRENDING ◆】

\beginalign \frac\partial \ell\partial \mu = \frac1\sigma^2\sum_i=1^n (y_i - \mu) \stackrelset= 0 \ \implies \hat\mu_MLE = \bary \endalign

\subsectionPart (a): Derive the log-likelihood Given $y_i \sim \mathcalN(\mu, \sigma^2)$ i.i.d., the log-likelihood is: TsLatex Rayanne Lenox

% Matrices \beginpmatrix a & b \ c & d \endpmatrix TsLatex Rayanne Lenox

\begindocument \maketitle

\sectionMaximum Likelihood Estimation