We estimated the probabilities of potential domain interactions Pr(D i,j = 1) by maximizing the likelihood of observed genetic interactions using the Expectation-Maximization (EM) algorithm [ 17- 19].
24 Estimations were made by maximizing the likelihood of the data, using the stochastic approximation expectation maximization algorithm followed by importance sampling to obtain the objective function value for hypothesis testing.
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).
We fit model parameters for individual RGCs by maximizing the likelihood of observed spike responses to a stochastic visual stimulus.
The estimation of the parameters of the MLP can be made by maximizing the likelihood of the model.
Online maximum likelihood estimator of time-varying delay is developed in which coefficients of the transformed delay signal are obtained by maximizing the likelihood function in terms of these DCT coefficients.
The MLE method gives a good estimate of the unknown parameters by maximizing the likelihood of the data we observe.
MLE estimates model parameters by maximizing the likelihood that the predicted probability of the event matches the actual one.
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.
A single transform, M s, is estimated for each speaker or speaker cluster s by maximizing the likelihood of observing the data from s, given the model.
The best warp factor for speaker is estimated by maximizing the likelihood of the warped features with respect to the HMM model and transcriptions : (9).
