Sentence examples for using the current estimate from inspiring English sources

Calculating the spill to date using the current estimate, and factoring in the approximately 365,000 barrels collected so far from the wellhead, results in a total of about 1.9 million to 3.5 million barrels, or about 80 million to 150 million gallons since the rig exploded on April 20.

4. Carry out a single cycle of ICM using the current estimate of, θ X and θ Y.

Given this initial estimate, the observed survival times of patients who switched from control to experimental treatment are transformed using the current estimate for e ψ and equation (2).

The authors stressed that the Expectation Maximisation (EM) algorithm sometimes involves complex numerical integration, especially during step E (the algorithm computes the expectation of the log-likelihood evaluated using the current estimate for the parameters), and that MI has the advantage of being computationally much simpler for situations with incomplete continuous variables.

We first used the current estimate of the incidence rate age curve to convert the observed age groups into the standard age groups.

And the rotor speed is estimated using the current estimation errors and the observed rotor fluxes based on the Lyapunov stability theory.

The newly arrived peer uses the current estimated size to determine its fanout value.

The difference between modified MASP and ICM are that ICM use MRF to model the probability function P(x = k| N c ), which is later estimated using current estimated labels, where as modified MASP uses the current estimated labels directly to estimate the probability P(N c | x = k).

Since the model for X with Zs and Y contains the parameters of interest, the weighted likelihood function can be iteratively maximized: estimating the weights using the current parameter estimates; and constructing the weighted likelihood function, which can then be maximized to update the parameter estimates.

In order to realize obtained optimal input, authors have proposed a two-stage adaptive procedure, where one iterates between parameter estimation, on the one side, and experiment design using the current parameter estimates, on the other.

The algorithm alternates between creating a function for the expectation of the log-likelihood using the current parameter estimates – the E-step – and computing the parameters that maximize the expected log-likelihood – the M-step.