Bayesian Data Mining and Machine Learning

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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Ming-Hui Chen: University of Connecticut, Department of Statistics
Dipak K. Dey: University of Connecticut, Department of Statistics
Peter Müller: The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics
Dongchu Sun: University of Missouri-Columbia, Department of Statistics
Keying Ye: University of Texas at San Antonio, Department of Management Science and Statistics, College of Business

Chapter Chapter 10 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 327-375 from Springer

Abstract: Abstract Researchers in machine learning have developed methods for largely automated inference with large data sets. With increasingly more powerful computing resources and ever increasing needs for statistical inference for massive data sets, similar methods are also being developed by researchers in Bayesian analysis. The distinction between machine learning and Bayesian analysis is starting to blur. This chapter discusses several examples of such research.

Keywords: Dirichlet Process; Latent Topic; Principal Component Analysis Model; Relevance Vector Machine; Adjusted Rand Index (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4419-6944-6_10

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DOI: 10.1007/978-1-4419-6944-6_10

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