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In this paper we consider an expectation-maximization method for maximizing the likelihood of (time nonhomogeneous) evolutionary Markov models on trees.
We estimate array C and matrix M by maximizing the likelihood l, which can be solved by using the expectation-maximization (EM) algorithm (Dempster et al., 1977).
Expectation maximization (EM; Dempster et al., 1977) is the most popular method for maximizing the likelihood.
Our design aims at maximizing the likelihood of a focal-point effect.
The estimation of the parameters of the MLP can be made by maximizing the likelihood of the model.
Such an approach will assist a supplier in effectively responding to RFQs, thereby maximizing the likelihood of winning future contracts.
The MLE method gives a good estimate of the unknown parameters by maximizing the likelihood of the data we observe.
On the other hand, c i and d i need to be estimated by numerically maximizing the likelihood function.
MLE estimates model parameters by maximizing the likelihood that the predicted probability of the event matches the actual one.
The best BLT parameters were estimated by a Gaussian mixture model (GMM) as the one maximizing the likelihood of the incoming data [11, 12].
The joint estimation of the model parameters can be obtained by maximizing the Likelihood function of the joined sample, with the hypothesis that the two samples are independent.
More suggestions(15)
optimize the likelihood
optimizes the likelihood
maximizing the storage
maximizing the amount
maximizing the experience
maximizing the transfer
maximizing the value
maximizing the revenue
maximizing the contrast
maximizing the window
maximizing the land
maximizing the Csum
maximizing the entropy
maximizing the capital
maximizing the network
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