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N. B. Asadi, T. H. Meng and W. H. Wong, “Reconfigur able Computing for Learning Bayesian Networks,” Proceedings of 16th International ACM/SIGDA Symposium on Field Programmable Gate Arrays, Monterey, 24-26 February, 2008, pp. 203-211.

has been cited by the following article:

• TITLE: Minimum Description Length Methods in Bayesian Model Selection: Some Applications

  AUTHORS: Mohan Delampady

  KEYWORDS: Bayesian Analysis; Model Selection; Minimum Description Length; Hierarchical Bayes; Bayesian Computations

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.2, April 26, 2013

  ABSTRACT: Computations involved in Bayesian approach to practical model selection problems are usually very difficult. Computational simplifications are sometimes possible, but are not generally applicable. There is a large literature available on a methodology based on information theory called Minimum Description Length (MDL). It is described here how many of these techniques are either directly Bayesian in nature, or are very good objective approximations to Bayesian solutions. First, connections between the Bayesian approach and MDL are theoretically explored; thereafter a few illustrations are provided to describe how MDL can give useful computational simplifications.