Multivariate Bayesian hypothesis testing for ground motion model selection

In this paper, the Bayesian hypothesis testing basis is proposed for selecting, ranking, and assigning weights to ground motion prediction equations that fits perfectly on the classical definition of a logic tree

Mohammad Sadegh Shahidzadeh

2020

Scholarcy highlights

  • In this paper, the Bayesian hypothesis testing basis is proposed for selecting, ranking, and assigning weights to ground motion prediction equations that fits perfectly on the classical definition of a logic tree
  • Accounting for data correlation is important in model ranking and combination which is missing from the commonly used scoring procedures such as the median likelihood, average log-likelihood, Euclidean distance ranking, and the Bayesian information criterion methods
  • The proposed method is applied to subsets of the NGA-West2 dataset, and five selected NGA-West2 models are ranked and weighted in different magnitude and period ranges according to available data
  • Shahidzadeh MS, Yazdani A A Bayesian updating applied to earthquake ground-motion prediction equations for Iran

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