Mixtures: Estimation and Applications

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 TitteringtonChapter 2 Online Expectation Maximisation (pages 31–53): Olivier CappeChapter 3 The Limiting Distribution of the EM Test of the Order of a Finite Mixture (pages 55–75): Jiahua Chen and Pengfei LiChapter 4 Comparing Wald and Likelihood Regions Applied to Locally Identifiable Mixture Models (pages 77–100): Daeyoung Kim and Bruce G. LindsayChapter 5 Mixture of Experts Modelling with Social Science Applications (pages 101–121): Isobel Claire Gormley and Thomas Brendan MurphyChapter 6 Modelling Conditional Densities Using Finite Smooth Mixtures (pages 123–144): Feng Li, Mattias Villani and Robert KohnChapter 7 Nonparametric Mixed Membership Modelling Using the IBP Compound Dirichlet Process (pages 145–160): Sinead Williamson, Chong Wang, Katherine A. Heller and David M. BleiChapter 8 Discovering Nonbinary Hierarchical Structures with Bayesian Rose Trees (pages 161–187): Charles Blundell, Yee Whye Teh and Katherine A. HellerChapter 9 Mixtures of Factor Analysers for the Analysis of High?Dimensional Data (pages 189–212): Geoffrey J. McLachlan, Jangsun Baek and Suren I. RathnayakeChapter 10 Dealing with Label Switching under Model Uncertainty (pages 213–239): Sylvia Fruhwirth?SchnatterChapter 11 Exact Bayesian Analysis of Mixtures (pages 241–254): Christian P. Robert and Kerrie L. MengersenChapter 12 Manifold MCMC for Mixtures (pages 255–276): Vassilios Stathopoulos and Mark GirolamiChapter 13 How many Components in a Finite Mixture? (pages 277–292): Murray AitkinChapter 14 Bayesian Mixture Models: A Blood?Free Dissection of a Sheep (pages 293–308): Clair L. Alston, Kerrie L. Mengersen and Graham E. Gardner