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Therefore, to maximize the observations included in our models, we did not include PIR as a covariate (100 missing observations).
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Discriminative training has become a common modelling method when the main principle is based on formulating an objective function tied to the classification and minimizing the recognition error directly instead of maximizing the observation likelihood.
We train the HMM using Baum-Welch algorithm [31] which consists of maximizing the observation expectation: {Lambda}^{opt}=mathit{arg} {max}_{Lambda}left({sum}_{sin left[1,Sright]} logPleft({mathcal{O}}_sright|Lambda Big right), (17).
For example, we can use the Viterbi algorithm [ 14] to predict the optimal hidden state sequence that maximizes the observation probability of the sequence pair (x, y).
The ideal position would maximize the number of observations made while minimizing the number of false observations.
MLE is the method of fitting parameters to model a series of observations that maximize the likelihood of those observations given the parameters.
The aim of this method is to determine the parameters that maximize the probability of observations.
This interval was chosen to maximize the number of observations available from each group.
To maximize the number of observations that could be included, a random day of birth was generated for these children.
The objective at each split is to maximize the proportion of observations with one of the outcome categories in the resulting nodes.
Statistical power at the 0.05 level required a minimum of 30 observations per device, and the study was designed to maximize observations (30 observations each for Dose 1 and Dose 2) while minimizing purchase and wastage relative to study product costs.
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