Degree Type
Thesis
Date of Award
2010
Degree Name
Master of Science
Department
Computer Science
First Advisor
Jin Tian
Abstract
Bayesian networks are being widely used in various data mining tasks for probabilistic inference and causual modeling [Pearl (2000), Spirtes et al. (2001)]. Learning the best Bayesian network structure is known to be NP-hard [Chickering (1996)]. Also, learning the single best Bayesian network structure does not always give a good approximation of the actual underlying structure. This is because in many domains, the number of high-scoring models is usually large.
In this thesis, we propose that learning the top-k Bayesian network structures and model averaging over these k networks gives a better approximation of the underlying model. The posterior probability of any hypotheses of interest is computed by averaging over the top-k Bayesian network models. The proposed techniques are applied to flow cytometric data to make causal inferences in human cellular signaling networks. The causal inferences made about the human T-cell protein signaling model by this method is compared with inferences made by various other learning techniques which were proposed earlier [Sachs et al. (2005)]. We also study and compare the classication accuracy of the top-k networks to that of the single MAP network.
In summary, this thesis describes:
1. Algorithm for learning the top-k Bayesian network structures.
2. Model averaging based on the top-k networks.
3. Experimental results on the posterior probabilities of the top-k networks.
4. How the top-k Bayesian networks can be applied to learn protein signaling networks with Results of top-k model averaging on the CYTO data.
5. Results of Classication Accuracy of the top-k networks.
DOI
https://doi.org/10.31274/etd-180810-3087
Copyright Owner
Lavanya Ram
Copyright Date
2010
Language
en
Date Available
2012-04-30
File Format
application/pdf
File Size
55 pages
Recommended Citation
Ram, Lavanya, "Bayesian model averaging using k-best bayesian network structures" (2010). Graduate Theses and Dissertations. 11879.
https://lib.dr.iastate.edu/etd/11879