Mixture Estimation and Applications

We have now completed the edition of the book Mixture Estimation and Applications with Kerrie Mengersen and Mike Titterington, made of contributions from participants to  the ICMS workshop on mixtures that took place in Edinburgh last March. Here is the prospective table of contents:

1. The EM Algorithm, Variational Approximations and Expectation Propagation for Mixtures, D.M. Titterington
2. Online Expectation-Maximisation, O. Cappé
3. The limiting distribution of the EM-test of the order of a finite mixture, J. Chen and P. Li
4. Comparing Wald and Likelihood Regions Applied to Locally Identifiable Mixture Models, D. Kim and B. G. Lindsay
5. Mixture of Experts Modelling with Social Science Applications, I.C. Gormley and T.B. Murphy
6. Modelling Conditional Densities using Finite Smooth Mixtures, F. Li, M. Villani and R. Kohn
7. Nonparametric Mixed Membership Modelling Using the IBP Compound Dirichlet Process, S. Williamson, C. Wang, K.A. Heller, and D.M. Blei
8. Discovering Non-binary Hierarchical Structures with Bayesian Rose Trees, C. Blundell, Y.W. Teh, and K.A. Heller
9. Mixtures of factor analyzers for the analysis of high-dimensional data, G.J. McLachlan, J. Baek, and S.I. Rathnayake
10. Dealing with Label Switching under Model Uncertainty, S. Frühwirth-Schnatter
11. Exact Bayesian Analysis of Mixtures, C.P. Robert and K.L. Mengersen
12. Manifold MCMC for Mixtures, V. Stathopoulos and M. Girolami
13. How Many Components in a Finite Mixture? M. Aitkin
14. Bayesian Mixture Models: A Blood Free Dissection of a Sheep, C.L. Alston, K.L. Mengersen, and G.E. Gardner

The manuscript is now with the publisher, J. Wiley, and we hope to see the book published by early Spring (next year!)…