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RooStats is the statistical package of the ROOT data analysis framework used ubiquitously in High Energy Physics. These are the tools used that will be used to discover the Higgs boson, supersymmetry, or even extra space-time dimensions. The Large Hadron Collider at CERN is collecting the largest dataset in the history of science, and RooStats is providing the tools for the high-level statistical analysis of the data. The framework provides a data modeling language, which encourages collaborative statistical modeling.
- Graphical Models, Bayesian Belief Networks, Gibbs sampler Various fields including high speed statistical physics, bio-informatics, speech processing and others involve the use of huge models of variables linked in complex ways. Probabilistic Graphical Models provide a general methodology for solving these problems. Bayesian networks, are one class of these models. The present document proposes a full methodology to implement these networks and sampling techniques and integrate them with RooStats. The aim is to equip RooStats with a new statistical tool, in the form of these Bayesian Belief Networks, in order to reduce the complexity being faced in the existing methods. The document suggests a method to construct these networks efficiently from given data and probability density functions as currently represented in RooStats., according to: An Algorithm for Bayesian Belief Network Construction from Data depending on present probability density functions, a method to store them , and lastly, implementations of a sampling algorithm to further use these networks. The sampling algorithm I propose to implement as a part of the project: Adaptive Importance Sampling, AIS-BN algorithm.