Selected article for: "Bernoulli process and RT PCR channel"
Author: Junan Zhu; Kristina Rivera; Dror Baron
Title: Noisy Pooled PCR for Virus Testing
  • Document date: 2020_4_11
    • ID: f07zk05y_9
    Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.06.20055384 doi: medRxiv preprint Fig. 1 . System model. A Bernoulli process with probability ρ of sickness generates an input vector x ∈ {0, 1} N , which reflects patient illness status. The input is multiplied by a measurement matrix, A ∈ {0, 1} M ×N , resulting in noiseless measurements, w = Ax ∈ N M (1), which are processed by.....
    Document: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.06.20055384 doi: medRxiv preprint Fig. 1 . System model. A Bernoulli process with probability ρ of sickness generates an input vector x ∈ {0, 1} N , which reflects patient illness status. The input is multiplied by a measurement matrix, A ∈ {0, 1} M ×N , resulting in noiseless measurements, w = Ax ∈ N M (1), which are processed by an RT-PCR channel, resulting in noisy measurements, y ∈ {0, 1} M (2). GAMP [7] processes the input channel relating ρ and x with g in (·) (4), and the output channel relating w and x with gout(·) (5) (details in Sec. III).

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