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M-step: Model parameters are updated by maximizing 〈Lc〉 w.r.t.
In the M-step, the parameters are updated by maximizing the log-likelihood of the complete data (words and documents) with respect to the probabilistic model.
The allele frequencies Φ and the inbreeding coefficient ρ are updated by maximizing E G, S | W, Ψ (t ) [ ln ℙ (G, S | Φ, ρ ) ], resulting in Equations 19 and 22 after some algebra.
In the M-step, the dropout rates Γ are updated by maximizing E Z | W, Ψ (t ) [ ln ℙ Z | ΓΓ ) ], resulting in Equations 20 and 21, quantities that can be obtained intuitively by considering each dropout as an independent Bernoulli trial.
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In the M-step, parameters are updated by the value that maximizes the expectation from E-step.
Update σ e 2 and σ θ 2. The updating formulas are obtained by maximizing an approximation to the marginal likelihood of the data p (y | σ e 2, σ θ 2 ) (the "evidence") given by the denominator of Equation 5. Iterate between (1) and (2) until convergence.
We thus updated our mutation detection strategy by maximizing efficiency (fewer false and unknown mutants) at the cost of decreasing the total number of true mutants.
Companies seek profit maximization by maximizing revenue and minimizing costs.
In M-step, the expected likelihood function obtained in E-step is maximized and the current estimates of parameters are updated by the new ones.
Updated by Katie Martin (2018).
Updated by Phil Wang, October 15 , 1998
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