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Once we find maximum likelihood values of and, i.e., a solution of the l1-regularized LRP, we can predict the probability of the two possible outcomes,, given a new feature vector, using the associated logistic model.
The cross indicates the position of the maximum likelihood values.
This investigation provides us with the 2D function χ 2 ε,M), whose minimum corresponds to the maximum likelihood values for the mean magnetic thickness and the mean magnetization.
To assess the uncertainty induced by each submodel, we carried out a sensitivity analysis where the parameters of each submodel are set to their maximum likelihood values one after the other, making the submodel deterministic while the rest of the model remains stochastic.
Indeed the parameters were estimated either via maximum likelihood values or via the likelihood surface [2].
The LRT statistic was calculated as twice the difference in maximum likelihood values (2Δℓ) between nested models.
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The relative fit of the parameters estimated by each of these models of codon substitution is represented by a maximum likelihood value that can be compared between nested hypotheses by a Likelihood Ratio Test (LRT).
With a sufficiently large ensemble (determined through experimentation), the mean state should represent the maximum likelihood value for the process at the time.
In the expression above, k is the number of independently adjusted parameters and L is the maximum likelihood value for n number of regular observations.
The box plots represent the distribution of the net carbon balance when parameters of each submodel are set to their maximum likelihood value.
It takes the glossary space information and channel spectral situation i.e., the SNR value from the environment, as an input to determine the most proper glossaries set in the glossary space by computing the maximum likelihood value.
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