Sarah Gerster: Learning Graphs from Data: A comparison of Different Algorithms with Application to Tissue Microarray Expreriments

Adviser: Peter Bühlmann

August 2007

Abstract:

A new algorithm (logilasso) to learn network structures from data has been introduced in “Penalized Likelihood and Bayesian Methods for Sparse Contingency Tables with an Application to Full-Length cDNA Libraries” (Dahinden, Parmigiani, Emerick and Buehlmann, 2007). The main idea is to study the interactions between the variables by performing a model selection in log-linear models.

In this master thesis, a few other graphical model fitting algorithms are compared to the logilasso. The chosen algorithms are the PC, the Max-Min-Hill-Climbing (MMHC) and the Greedy Equivalent Search (GES). They all base on different approaches to fit a graphical model. Those methods are presented and the algorithms are described. Their performance, in the sense of their ability to reconstruct a graph, is tested on simulated data. The algorithms are also applied to Renal Cell Carcinoma data to
illustrate a typical domain of application for such algorithms.

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