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Discovering non-binary hierarchical structures with Bayesian rose trees

Blundell, C; Teh, YW; Heller, KA; (2011) Discovering non-binary hierarchical structures with Bayesian rose trees. In: Mengersen, K and Robert, CP and Titterington, M, (eds.) Mixture: Estimation and Applications. (pp. 161-187). John Wiley & Sons: Chichester, UK.

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Abstract

Book description: This book uses the EM (expectation maximization) algorithm to simultaneously estimate the missing data and unknown parameter(s) associated with a data set. The parameters describe the component distributions of the mixture; the distributions may be continuous or discrete. The editors provide a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions along with MCMC computational methods, together with a range of detailed discussions covering the applications of the methods and features chapters from the leading experts on the subject. The applications are drawn from scientific discipline, including biostatistics, computer science, ecology and finance. This area of statistics is important to a range of disciplines, and its methodology attracts interest from researchers in the fields in which it can be applied.

Type: Book chapter
Title: Discovering non-binary hierarchical structures with Bayesian rose trees
ISBN-13: 9781119993896
DOI: 10.1002/9781119995678.ch8
Publisher version: http://eu.wiley.com/WileyCDA/WileyTitle/productCd-...
UCL classification: UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
URI: http://discovery.ucl.ac.uk/id/eprint/161861
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