Hence, with optimal ordering (the weaker user gets decoded with no interference), the lower bound on the average transmit power can be obtained by computing the expectations in the following expression: p L ( 2, K ) = γ E F ∥ h ∥ 2 ( M, 2, K ; x ) 1 x + E F ∥ h ∥ 2 ( M, 1, K ; x ) 1 x E F sin 2 θ 1 ( M, 1, K - 1 ; x ) 1 x (53).
If we denote the average transmit power, required to satisfy target SINRs at K s users selected through NUS from a pool of K users, by pN(K s, K), this can be obtained by computing the expectations in the following expression: p N ( K s, K ) ≤ γ ∑ i = 1 K s E F ∥ h ∥ 2 ( M, K s + 1 - i, K ; x ) 1 x E F sin 2 θ ( i - 1 ) ( M ; x ) 1 x.
When (mathcal{Y}(t)) is commutative, i.e., (y(t)) is a classical measurement process (by the spectral theorem [32], Theorem 3.3), the optimal filter in the least squares sense is obtained by computing the conditional expectation onto (mathcal{Y}(t)) [5, 32].
One might select the model order K by arg max p(K| x, y) with K ∈{0,1,…, Kmax} and also can perform parameter estimation by computing the conditional expectation E(θ K | x, y) based on (10) shown subsequently.
In the case of a single flow (i.e., ) we can easily carry out value iteration numerically, by discretizing the argument and values of and computing the expectation and maximization numerically.
We tested this expectation by computing the Spearman rank correlation between the length of each ORF in the genome and the quantile of its true mRNA folding energy compared with the null distribution.
Random genome-wide expectation for eQTL was calculated by computing the number of eQTL associated with a random set of 537 genes (the number of genes which overlap CNV regions).
Parameters were estimated by computing the maximum likelihood estimation of the parameters without any approximation of the model (that is, no linearization) using the stochastic approximation expectation maximization algorithm combined with a Markov chain Monte Carlo procedure.
You measure the opportunity cost by computing the value of the road not taken.
After that, we compute the expectation as our final prediction for the query target.
