Mixture estimation and applications [stimulated by a workshop entitled 'Mixture Estimation and Applications' held at the International Centre for Mathematical Science (ICMS) in Edinburgh on 3-5 March 2010

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. Content: Chapter 1 The EM Algorithm, Variational Approximations and Expectation Propagation for Mixtures (pages 1–29): D. Michael Titterington Chapter 2 Online Expectation Maximisation (pages 31–53): Olivier Cappe Chapter 3 The Limiting Distribution of the EM Test of the Order of a Finite Mixture (pages 55–75): Jiahua Chen and Pengfei Li Chapter 4 Comparing Wald and Likelihood Regions Applied to Locally Identifiable Mixture Models (pages 77–100): Daeyoung Kim and Bruce G. Lindsay Chapter 5 Mixture of Experts Modelling with Social Science Applications (pages 101–121): Isobel Claire Gormley and Thomas Brendan Murphy Chapter 6 Modelling Conditional Densities Using Finite Smooth Mixtures (pages 123–144): Feng Li, Mattias Villani and Robert Kohn Chapter 7 Nonparametric Mixed Membership Modelling Using the IBP Compound Dirichlet Process (pages 145–160): Sinead Williamson, Chong Wang, Katherine A. Heller and David M. Blei Chapter 8 Discovering Nonbinary Hierarchical Structures with Bayesian Rose Trees (pages 161–187): Charles Blundell, Yee Whye Teh and Katherine A. Heller Chapter 9 Mixtures of Factor Analysers for the Analysis of High?Dimensional Data (pages 189–212): Geoffrey J. McLachlan, Jangsun Baek and Suren I. Rathnayake Chapter 10 Dealing with Label Switching under Model Uncertainty (pages 213–239): Sylvia Fruhwirth?Schnatter Chapter 11 Exact Bayesian Analysis of Mixtures (pages 241–254): Christian P. Robert and Kerrie L. Mengersen Chapter 12 Manifold MCMC for Mixtures (pages 255–276): Vassilios Stathopoulos and Mark Girolami Chapter 13 How many Components in a Finite Mixture? (pages 277–292): Murray Aitkin Chapter 14 Bayesian Mixture Models: A Blood?Free Dissection of a Sheep (pages 293–308): Clair L. Alston, Kerrie L. Mengersen and Graham E. Gardner Mengersen (Queensland U. of Technology, Australia), Robert (U. Paris-Dauphine, France), and Titterington (U. of Glasgow, UK) present, alongside two of their own contributions, 12 papers from a March 2010 workshop held at the International Centre for Mathematical Sciences in Edinburgh, Scotland on the use of statistical mixture distributions for modeling scenarios in which certain variables are measured but a categorical variable is missing (such as when clinical data on a patient is available but their disease category is not), as well as variations such as when the missing variable follows a Markov chain model and latent structure models in which the missing variable or variables represent model-enriching devices rather than real physical entities. The papers explore methodological and applied issues of statistical mixture distributions. Both Bayesian and non-Bayesina methods are addressed and prominent application areas include biology and economics. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

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.