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.
About the author
Kerrie L. Mengersen, Queensland University of Technology, Australia.
Christian P. Robert, Universite Paris-Dauphine, France.
D. Michael Titterington, University of Glasgow, Scotland.
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