Selected article for: "posterior model probability and present work employ"
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_4
    Snippet: The traditional approaches of hypotheses testing, when applied to model selection, have been often found to be mediocre [6, 7] . The adjusted coefficient of multiple determination that is often used in model selection was found to be a very poor approach [8] . Posada and Buckley [9] pointed out that the Bayesian and Akaikes information criterion (AIC) approaches present several important advantages over other model selection methods. Therefore, i.....
    Document: The traditional approaches of hypotheses testing, when applied to model selection, have been often found to be mediocre [6, 7] . The adjusted coefficient of multiple determination that is often used in model selection was found to be a very poor approach [8] . Posada and Buckley [9] pointed out that the Bayesian and Akaikes information criterion (AIC) approaches present several important advantages over other model selection methods. Therefore, in the present work we employ the Bayes factors to select one model from a set of competing models which can capture the underlying disease outbreak best, and further it can be confirmed by calculating AIC values. The basis of the Bayes factor approach to model selection is quantifying the plausibility of each model when the data and the set of candidate models are given. The Bayes factor is a measure of the change from prior model odds to posterior model odds, brought about by the observed data. In this study, we calculate the Bayes factor with the ratio of the selected number of different models and sample from the joint space of product of model and parameters in each model and then estimate the posterior probability of each model using Metropolis-Hastings (MH) algorithms.

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