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Scale-free network priors in Bayesian inference with applications to bioinformatics
JSAI Technical Report, Type 2 SIG
This paper integrates scale-free network properties into statistical inference. This is accomplished in a meaningful manner by devising scale-free prior distributions based on three well-known scale-free network models in the framework of Gaussian graphical models. The new priors are compared with a random network prior via an extensive Markov chain Monte Carlo simulation. As well, a numerical example using microarray data to infer a protein-protein interaction network is provided.doi:10.11517/jsaisigtwo.2007.dmsm-a702_01 fatcat:ucpa3swvfzgvbex3bzgexoj5ty