The implication of the expected utility hypothesis, therefore, is that consumers and firms seek to maximize the expectation of utility rather than monetary values alone.
With the lexicographic method, we first maximize the expectation of the life-cycle NPV value, then we minimize the risk using the resulting optimal value of expected NPV as a constraint.
Our objective function is to maximize the expectation of by selecting the optimal transmit power vector and transmit rate vector that is, (2).
Strictly speaking, this algorithm is a generalized form of EM [31] because the M step increases but does not maximize the expectation of the log-likelihood of the hidden data.
EM algorithm attempts to maximize the expectation of the logarithm of the joint likelihood of the model.
Then, we introduce two estimators in order to maximize the expectation of G 2,1)(θ, y) or G 2,2)(θ, y) under the probability distribution p(2)(θ| x, D).
The goal is to maximize the expectation value of the net present value (NPV) at time τ, i.e., the objective function Φ weighted by the exponential probability density function with rate parameter λ, (5) max p E [ NPV ] ≔ max p ∫ 0 t f λ e − λ τ Φ d τ subject to the constraints given in (4) for all 0 ≤ τ ≤ t f.
Subject to normal tissue toxicity constraints, the overall objective is to maximize the expectation of differential cell killing, defined as the probability of killing a malignant cell relative to the probability of killing a normal cell (i.e. the odds), or the log thereof.
Given the hyper-parameters Ψ j (f) and m, the spatial covariance matrices R j (f) can be estimated in the MAP sense in step (17) of Algorithm 1 by maximizing the expectation of the log-posterior of the hidden data Q I W = γ ∑ j, f log I W ( R j ( f ) | Ψ j ( f ), m ) + ∑ j, n, f − tr ( Σ c j − 1 ( n, f ) R ̂ c j ( n, f ) ) − log | π Σ c j ( n, f ) | (18).
In the M-step, parameters are updated by the value that maximizes the expectation from E-step.
