2008 ABayesianMixtureModelwithLinear

• (Song et al., 2008) ⇒ Xiuyao Song, Chris Jermaine, Sanjay Ranka, and John Gums. (2008). “A Bayesian Mixture Model with Linear Regression Mixing Proportions.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401970

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• http://scholar.google.com/scholar?q=%22A+bayesian+mixture+model+with+linear+regression+mixing+proportions%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401970&preflayout=flat#citedby

Quotes

Abstract

Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is to learn how complex data distributions evolve. We develop models and Bayesian learning algorithms for inferring the temporal trends of the components in a mixture model as a function of time. We show the utility of our models by applying them to the real-life problem of tracking changes in the rates of antibiotic resistance in Escherichia coli and Staphylococcus aureus. The results show that our methods can derive meaningful temporal antibiotic resistance patterns.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ABayesianMixtureModelwithLinear	Xiuyao Song John Gums Sanjay Ranka Chris Jermaine			A Bayesian Mixture Model with Linear Regression Mixing Proportions				10.1145/1401890.1401970