The problem is maximized for patients with chronic illnesses.
The conditional log-likelihood 2010 can then be maximized to yield joint estimates for μ1, μ2, and μ3.
Since the model for X with Zs and Y contains the parameters of interest, the weighted likelihood function can be iteratively maximized: estimating the weights using the current parameter estimates; and constructing the weighted likelihood function, which can then be maximized to update the parameter estimates.
That way, value for money will be maximized.
Market size, for example, is a dimension to be maximized.
In the second step (M-step), the expected log-likelihood evaluated in the E-step is maximized and new estimates for the parameters are obtained.
After modification of the EM algorithm, the likelihood is not maximized any more, but the penalized likelihood is maximized conditional on the estimates of the smoothing parameters.
GoM parameters are estimated in an iterative method: firstly, the likelihood function is maximized with λkjl fixed, giving a first estimate of all gik, then, fixing gik, the likelihood is maximized to update the λkjl, which is repeated until convergence.
However, instead of maximizing the likelihood, a prior is incorporated on the model parameter vector θ, based on the clustering of S, and the posterior is maximized to get the Maximum A-Posteriori estimate for the parameters θ.
In the first case, we have prompt reaction for small α, while in the second, diffusion rate of the state estimates is maximized.
