A new algorithm in bayesian model averaging in regression models

Tsai Hung Fan, Guo Tzau Wang, Jenn Hwa Yu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We propose a new iterative algorithm, namely the model walking algorithm, to modify the widely used Occams window method in Bayesian model averaging procedure. It is verified, by simulation, that in the regression models, the proposed algorithm is much more efficient in terms of computing time and the selected candidate models. Moreover, it is not sensitive to the initial models.

Original languageEnglish
Pages (from-to)315-328
Number of pages14
JournalCommunications in Statistics - Simulation and Computation
Issue number2
StatePublished - 1 Jan 2014


  • Ayesian model averaging
  • Occams window
  • Regression models


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