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_18
Snippet: The distribution is called the instrumental (or proposal or candidate) distribution and probability ( ( ) , ) is the Metropolis-Hastings acceptance probability [16] . Suppose that the observed data is generated by a model ∈ M, where M is the finite set of competing models. Corresponding to model , there is a distinct unknown parameter vector of dimension and a prior model probability ≡ ( = ) with ∑ ∈M = 1. Let Θ be set of all possible va.....
Document: The distribution is called the instrumental (or proposal or candidate) distribution and probability ( ( ) , ) is the Metropolis-Hastings acceptance probability [16] . Suppose that the observed data is generated by a model ∈ M, where M is the finite set of competing models. Corresponding to model , there is a distinct unknown parameter vector of dimension and a prior model probability ≡ ( = ) with ∑ ∈M = 1. Let Θ be set of all possible values for ; then ∈ Θ ⊆ R ; and let be the collection of all model-specific 's. Now our interest lies in obtaining the posterior probabilities for the various models, ( = | ) and then in determining the best model.
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