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In business and for engineering economics in both industrial engineering and civil engineering practice, the minimum acceptable rate of return, often abbreviated MARR, or hurdle rate is the minimum rate of return on a project a manager or company is willing to accept before starting a project, given its risk and the opportunity cost of forgoing other projects. [1]
In mathematics, economics, and computer science, the Gale–Shapley algorithm (also known as the deferred acceptance algorithm, [1] propose-and-reject algorithm, [2] or Boston Pool algorithm [1]) is an algorithm for finding a solution to the stable matching problem.
In numerical analysis and computational statistics, rejection sampling is a basic technique used to generate observations from a distribution.It is also commonly called the acceptance-rejection method or "accept-reject algorithm" and is a type of exact simulation method.
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In general, acceptance sampling is employed when one or several of the following hold: [2] testing is destructive; the cost of 100% inspection is very high; and; 100% inspection takes too long. A wide variety of acceptance sampling plans is available. For example, multiple sampling plans use more than two samples to reach a conclusion.
The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio.
The Bennett acceptance ratio method (BAR) is an algorithm for estimating the difference in free energy between two systems (usually the systems will be simulated on the computer). It was suggested by Charles H. Bennett in 1976.
Lot quality assurance sampling (LQAS) is a random sampling methodology, originally developed in the 1920s [1] as a method of quality control in industrial production. Compared to similar sampling techniques like stratified and cluster sampling, LQAS provides less information but often requires substantially smaller sample sizes.