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The model is presented as a maximum likelihood problem involving discrete variables.
The demapping task is often posed as a maximum likelihood problem [6].
The proposed GIR algorithm conducts motion correction by solving the maximum likelihood problem.
The distribution can be estimated by formulating a maximum likelihood problem [30, 31], where the parameters in a family of distributions are optimized to fit the data.
We proposed an alternating minimization (AM) algorithm based on turning a maximum likelihood problem into a double minimization of I-divergence introduced by Csiszár [13].
For a sequence of states,, and frame parameters (where is a vector containing both the position and velocity at time ) this can be formulated as a maximum likelihood problem: (4).
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Cheng [4] showed that in unbounded likelihood problems such as estimation of three-parameter gamma, lognormal or Weibull distributions, the MSP estimation method produces consistent and asymptotically efficient estimators.
Also, Cheng [4] showed that in unbounded likelihood problems such as estimation of three-parameters in the Weibull distribution, the MSP estimation method produces consistent and asymptotically efficient estimators.
Because of initial likelihood problems at the start of the analyses we used a starting tree obtained from the Bayesian analysis of Massoni et al. [ 33] and rendered ultrametric using Penalized Likelihood (PL) implemented in r8s v1.8 [ 91].
The LLBO is concave in each parameter; thus, convergence properties of this iterative optimization algorithm, also called the variational Bayesian expectation-maximization algorithm, are similar to those of the expectation-maximization algorithm for maximum-likelihood problems.
In the not-so-distant future, early identification of alcohol problems through predictive algorithms by supercomputers connected to a mobile application will warn individuals and providers of the likelihood of problem use long before problems start.
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