\f14. Maximum likelihood estimation (MLE) For a regular parametric model, we suppose that 39-12:, El) with 9 e (E) c Rd is the probability mass or density function of the data X when H is true. of For the data X, the likelihood function 390% = xi). discrete. 1209,32): pineal = 'i' H irt-13}, absolutelyr continuous, J'=] serves as a measure of how 'likely' the probability distribution P9 is to have produced the observed data X = 3:. a The tX} maximizing the likelihood function serves as an estimate of the true 3. That is, L((:E), :c} = max L053, :12) for the sample X = 3:. see ~ I In principle, tvlLE employs distribution P3- 5 P to estimate the true one P9 behind X. g CI Homewonktl: Complete Problem 2.2.11. .; __ _ _'l