Selected article for: "average number and case average number"
Author: Fayyaz Minhas; Dimitris Grammatopoulos; Lawrence Young; Imran Amin; David Snead; Neil Anderson; Asa Ben-Hur; Nasir Rajpoot
Title: Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling
  • Document date: 2020_4_14
    • ID: 7rip6wtu_11
    Snippet: Based on our computational analysis, the expected number of tests required for a given positive probability and input samples (under no belief assumptions) can be calculated as: ( , ) = 2( − 1)(1 − 2 −4.5 ) + 1. This formulation captures the typical average case number of required tests using the proposed strategy. It can be seen that this formulation is heavily dependent on the value of the positive probability. However, it can significant.....
    Document: Based on our computational analysis, the expected number of tests required for a given positive probability and input samples (under no belief assumptions) can be calculated as: ( , ) = 2( − 1)(1 − 2 −4.5 ) + 1. This formulation captures the typical average case number of required tests using the proposed strategy. It can be seen that this formulation is heavily dependent on the value of the positive probability. However, it can significantly reduce the number of required tests when the probability is small, e.g., for community level testing. The probability value up to which the proposed strategy can remain effective, i.e., up till T
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