Sentence examples for minimise the expected from inspiring English sources

In this paper we apply Bayesian decision-making approaches to conformity assessment, using a loss function to quantify the cost of wrong decisions and deriving optimal decision rules that minimise the expected loss.

The Dynamic Overflow Risk Assessment (DORA) strategy aims to minimise the expected Combined Sewer Overflow (CSO) risk by considering (i) the water volume presently stored in the drainage network, (ii) the expected runoff volume (calculated by radar-based nowcast models) and – most important – (iii) the estimated uncertainty of the runoff forecasts.

In this paper, a two-player non-cooperative game is envisaged between on the one hand the network user seeking a path to minimise the expected trip cost and on the other hand an "evil entity" choosing link performance scenarios to maximise the expected trip cost.

The bit allocation should be chosen to minimise the expected distortion.

34 35 This approach often lets go of the orthogonality constraint and attempts to minimise the expected asymptotic variance covariance (AVC) matrix of the design.

22 23 This approach relaxes the orthogonality constraint and attempts to minimise the expected asymptotic variance covariance (AVC) matrix of the design.

In life-cycle costing analyses, optimal design is usually achieved by minimising the expected value of the discounted costs.

The objective is to design optimal planned lead-times by minimising the expected sum of inventory holding costs and tardiness cost.

The conventional optimal design, for given type 1 and type 2 error rates, is the one which minimises the expected sample size under the null hypothesis.

Identifying MAMS designs which maintain the overall operating characteristics but have desirable properties such as minimising the expected or maximum sample sizes is an area of ongoing research.

The probability that y belongs to class k is just the posterior probability p k| y), which by Bayes' Theorem can be written as (2) p (k | y ) = π ^ k f k (y | θ ^ k ) ∑ c = 1 C π ^ c f c (y | θ ^ c ) Assigning y to the class which maximises this posterior probability (the maximum probability criterion) minimises the expected misclassification error.