First, that the expectations of the data subject can serve to relax informational obligations.
Each vij in the matrix V, represents the expectations of the data transmission rate between the device in network i and cloud resource j, and it takes a weighted average of the history data Vij, as shown in formula (4).
And there are two matrices V and RTT that record the expectations of the data transmission rate and the round trip time respectively, as shown in formula (2) and (3).
In each iteration, the expectation of the full data log-likelihood conditional on the parameters from the previous iteration, is maximized to obtain the optimal parameters for the current iteration.
This means that the natural logarithm of the marginal likelihood can be computed as the integral over the expectation of the logarithmized data likelihood within the model with respect to the power posterior.
We generalize the expression introduced by Bailey and Elkan (1994) to define the expectation of the missing data for position j in sequence i using the OOPS model as follows: (4) Z i, j (t ) ≜ p (Z i, j = 1 | X i, θ (t ) ) = p (X i | Z i, j = 1, θ (t ) ) ∑ l = 1 L i − w + 1 p (X i | Z i, l = 1, θ (t ) ) where Li is defined as the length of input sequence i, and w is defined as the motif width.
Define by the expectation of the complete-data log-likelihood function with respect to the unknown signal strength, given the observations and the current parameter estimate.
Given M, a sufficient statistic of the data, Z, and a truncation adaptive stopping rule (Liu & Hall, 1999) one calculates the expectation of the first stage data, Y1 say, given the pair to obtain the truncation adaptable UMVUE.
where μ indicates one of the parameter and 〈lnp X, θ)〉 θ μ is the expectation of the joint probability of the data and latent variables which take over all variables except μ.
By using a mixture of a distribution with zero effects and an exponential distribution as a prior for the marker effects, the integration involved in calculating the expectation of the breeding values given the data can be solved analytically, which makes non-MCMC estimation of GW-EBV possible.
