Selected article for: "posterior distribution and prior distribution"
Author: Hongzhe Zhang; Xiaohang Zhao; Kexin Yin; Yiren Yan; Wei Qian; Bintong Chen; Xiao Fang
Title: Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread
  • Document date: 2020_4_6
    • ID: ff4937mj_38
    Snippet: Having defined the general framework of the epidemic model with transformation functions, we next show how to learn its associated parameters, Θ = Θ H ∪ Θ f = (β, µ, γ, a, b). Specifically, we impose a prior distribution P (Θ) on Θ by resorting to existing knowledge on COVID-19 and obtain the posterior distribution of Θ given the reported discrete trajectory of official numbers [Q o t , R o t ] T +1 t=1 , where the short time window is.....
    Document: Having defined the general framework of the epidemic model with transformation functions, we next show how to learn its associated parameters, Θ = Θ H ∪ Θ f = (β, µ, γ, a, b). Specifically, we impose a prior distribution P (Θ) on Θ by resorting to existing knowledge on COVID-19 and obtain the posterior distribution of Θ given the reported discrete trajectory of official numbers [Q o t , R o t ] T +1 t=1 , where the short time window is from t = 1 to t = T + 1. Accordingly, we obtain the unnormalized posterior distribution

    Search related documents:
    Co phrase search for related documents
    • epidemic model and official number: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • epidemic model and posterior distribution: 1, 2, 3, 4, 5, 6, 7
    • epidemic model and prior distribution: 1, 2
    • epidemic model and short time: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
    • epidemic model and short time window: 1
    • epidemic model and transformation function: 1
    • epidemic model general framework and general framework: 1, 2, 3
    • general framework and short time: 1