Selected article for: "posterior distribution and prior probability"

Author: Liu, Wendi; Tang, Sanyi; Xiao, Yanni
Title: Model Selection and Evaluation Based on Emerging Infectious Disease Data Sets including A/H1N1 and Ebola
  • Document date: 2015_9_15
  • ID: 0j4is0n4_28
    Snippet: Further, we assume that the set of parameter vectors is = { , , } ( = 1, 2, 3, 4), in which the parameters are independent of each other. In particular, = 1 for models 1 and 2 . For simplicity, we select noninformation prior distribution; that is, ∝ constant; thus the posterior distribution probability reads.....
    Document: Further, we assume that the set of parameter vectors is = { , , } ( = 1, 2, 3, 4), in which the parameters are independent of each other. In particular, = 1 for models 1 and 2 . For simplicity, we select noninformation prior distribution; that is, ∝ constant; thus the posterior distribution probability reads

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