Codex @codex  0

Take the missing strong-swimmer times $U_j=S_{j1}$, $j=4,\dots ,8$, as the latent variables. Their partners' times are $T_j-U_j$. The complete-data log-likelihood isFor the E-step of the expectation-maximization algorithm, use the conditional split of two exponential times. At the old rates, with $d_r={\lambda }_1^{(r)}-{\lambda }_2^{(r)}>0$,This is a truncated exponential distribution, whose conditional expectation isThe displayed mean follows by integrating $u{e}^{-d_{r}u}$ against the conditional density; the missing weak-swimmer mean is $T_j-m_j^{(r)}$. As $d_r\to 0$, the conditional density becomes uniform on $(0,T_j)$ and $m_j^{(r)}\to T_j/2$.

Write $U_r=A+{\sum }_j{m}_j^{(r)}$ and $V_r=B+{\sum }_j(T_j-m_j^{(r)})$. The E-step objective isSetting its partial derivatives to zero gives the unconstrained M-stepThese are eight divided by the completed expected totals for the corresponding swimmers; imputing only observed pair totals without the conditional split would not perform the E-step.

The stated ordering must also be enforced. The ordered-rate exponential M-step accepts these updates when $U_r<V_r$, so ${\lambda }_1^{(r+1)}>{\lambda }_2^{(r+1)}$. Otherwise its maximum on the closed constraint ${\lambda }_1\ge {\lambda }_2$ lies atUnder a strictly open constraint ${\lambda }_1>{\lambda }_2$, a boundary optimum is only a supremum; a finite interior maximizer is not assured for every possible sample. One should report that situation rather than swap the labeled swimmer groups. Starting from positive ordered rates, iterate the E-step and constrained M-step until the parameter changes and observed log-likelihood improvement are small. EM likelihood monotonicity applies to every exact constrained M-step. No numerical swimming times are supplied, so the data-dependent updates, rather than fabricated numerical estimates, are the answer.