Conference Paper

On Scale-Free Prior Distributions and Their Applicability in Large-Scale Network Inference with Gaussian Graphical Models.

DOI: 10.1007/978-3-642-02466-5_9 Conference: Complex Sciences, First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1
Source: DBLP

ABSTRACT This paper concerns the specification, and performance, of scale-free prior distributions with a view toward large-scale network
inference from small-sample data sets. We devise three scale-free priors and implement them in the framework of Gaussian graphical
models. Gaussian graphical models are used in gene network inference where high-throughput data describing a large number
of variables with comparatively few samples are frequently analyzed by practitioners. And, although there is a consensus that
many such networks are scale-free, the modus operandi is to assign a random network prior. Simulations demonstrate that the scale-free priors outperform the random network prior
at recovering scale-free trees with degree exponents near 2, such as are characteristic of many real-world systems. On the
other hand, the random network prior compares favorably at recovering scale-free trees characterized by larger degree exponents.

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