Sentence examples for an expected utility of from inspiring English sources

An individual whose value is 70 has an expected utility of 0.75 (=1 × 12/(100–31)+0.70 × 57/(100–31)) in the event that he is diagnosed and treated for prostate cancer and therefore an anticipated loss in quality of life of 25%.

Every outcome that gives each gunner an expected utility of more than 1 seems rationally acceptable.

For example, if it is the case that #1 can make a claim and all #2 can do is to accept or refuse, #1 does best by offering #2 an expected utility of 1.00001 and claim 2.99999 for himself.

Their verdict in the case of the gunners is the same: the rational thing to agree to is a distribution that gives each gunner an expected utility of 2. (Note that this verdict does not tell the gunners how they should realize this outcome. There are two ways in which they could secure an expected outcome of (2, 2).

Due to the effects coding, all parameters are estimated relative to the grand mean, which has an expected utility of zero.

8 Suppose that patients (or their relatives) search among the set of nursing homes to find an expected utility maximising (net of search costs) combination of price and expected waiting time.

An incremental utility (IU) index can be defined as the difference between the respective expected utilities of both follow-up strategies returning a numerical value in the (−1, 1) range, that can be estimated with its 95% CI and tested against the hypothesis of IU = 0 (with our assumptions, a positive IU value was in favour of a higher expected utility of the HM strategy).

After all, an agent who can be represented as an expected utility maximizer with degrees of belief that obey the probability calculus, can also be represented as someone who fails to maximize expected utility with degrees of belief that violate the probability calculus.

We formulate personalization of the steering parameters as the maximization of an expected utility function.

We consider the personalization problem as linear regression from acoustic features to tuning parameters, and formulate learning in this model as the maximization of an expected utility function.

This interpretation sees credence as that function such that your elicited preferences and observed actions can be represented as those of an expected utility maximiser with respect to that probability function (Briggs 2014: section 2.2).