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Mixtures: Estimation and Applications

ISBN: 978-1-119-99389-6
330 pages
June 2011
Mixtures: Estimation and Applications (111999389X) cover image
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.

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Preface

Acknowledgements

List of Contributors 

1 The EM algorithm, variational approximations and expectation propagation for mixtures
D.Michael Titterington

1.1 Preamble

1.2 The EM algorithm

1.3 Variational approximations

1.4 Expectation-propagation

Acknowledgements

References

2 Online expectation maximisation
Olivier Cappé

2.1 Introduction

2.2 Model and assumptions

2.3 The EM algorithm and the limiting EM recursion

2.4 Online expectation maximisation

2.5 Discussion

References

3 The limiting distribution of the EM test of the order of a finite mixture
J. Chen and Pengfei Li

3.1 Introduction

3.2 The method and theory of the EM test

3.3 Proofs

3.4 Discussion

References

4 Comparing Wald and likelihood regions applied to locally identifiable mixture models
Daeyoung Kim and Bruce G. Lindsay

4.1 Introduction

4.2 Background on likelihood confidence regions

4.3 Background on simulation and visualisation of the likelihood regions

4.4 Comparison between the likelihood regions and the Wald regions

4.5 Application to a finite mixture model

4.6 Data analysis

4.7 Discussion

References

5 Mixture of experts modelling with social science applications
Isobel Claire Gormley and Thomas Brendan Murphy

5.1 Introduction

5.2 Motivating examples

5.3 Mixture models

5.4 Mixture of experts models

5.5 A Mixture of experts model for ranked preference data

5.6 A Mixture of experts latent position cluster model

5.7 Discussion

Acknowledgements

References

6 Modelling conditional densities using finite smooth mixtures
Feng Li, Mattias Villani and Robert Kohn

6.1 Introduction

6.2 The model and prior

6.3 Inference methodology

6.4 Applications

6.5 Conclusions

Acknowledgements

Appendix: Implementation details for the gamma and log-normal models

References

7 Nonparametric mixed membership modelling using the IBP compound Dirichlet process
Sinead Williamson, Chong Wang, Katherine A. Heller, and David M. Blei

7.1 Introduction

7.2 Mixed membership models

7.3 Motivation

7.4 Decorrelating prevalence and proportion

7.5 Related models

7.6 Empirical studies

7.7 Discussion

References

8 Discovering nonbinary hierarchical structures with Bayesian rose trees
Charles Blundell, Yee Whye Teh, and Katherine A. Heller

8.1 Introduction

8.2 Prior work

8.3 Rose trees, partitions and mixtures

8.4 Greedy Construction of Bayesian Rose Tree Mixtures

8.5 Bayesian hierarchical clustering, Dirichlet process models and product partition models

8.6 Results

8.7 Discussion

References

9 Mixtures of factor analyzers for the analysis of high-dimensional data
Geoffrey J. McLachlan, Jangsun Baek, and Suren I. Rathnayake

9.1 Introduction

9.2 Single-factor analysis model

9.3 Mixtures of factor analyzers

9.4 Mixtures of common factor analyzers (MCFA)

9.5 Some related approaches

9.6 Fitting of factor-analytic models

9.7 Choice of the number of factors q

9.8 Example

9.9 Low-dimensional plots via MCFA approach

9.10 Multivariate t-factor analysers

9.11 Discussion

Appendix

References

10 Dealing with Label Switching under model uncertainty
Sylvia  Frühwirth-Schnatter

10.1 Introduction

10.2 Labelling through clustering in the point-process representation

10.3 Identifying mixtures when the number of components is unknown

10.4 Overfitting heterogeneity of component-specific parameters

10.5 Concluding remarks

References

11 Exact Bayesian analysis of mixtures
Christian .P. Robert and Kerrie L. Mengersen

11.1 Introduction

11.2 Formal derivation of the posterior distribution

References

12 Manifold MCMC for mixtures
Vassilios Stathopoulos and Mark Girolami

12.1 Introduction

12.2 Markov chain Monte Carlo methods

12.3 Finite Gaussian mixture models

12.4 Experiments

12.5 Discussion

Acknowledgements 

Appendix

References

13 How many components in a finite mixture?
Murray Aitkin

13.1 Introduction

13.2 The galaxy data

13.3 The normal mixture model

13.4 Bayesian analyses

13.5 Posterior distributions for K (for flat prior)

13.6 Conclusions from the Bayesian analyses

13.7 Posterior distributions of the model deviances

13.8 Asymptotic distributions

13.9 Posterior deviances for the galaxy data

13.10 Conclusion

References

14 Bayesian mixture models: a blood-free dissection of a sheep
Clair L. Alston, Kerrie L. Mengersen, and Graham E. Gardner

14.1 Introduction

14.2 Mixture models

14.3 Altering dimensions of the mixture model

14.4 Bayesian mixture model incorporating spatial information

14.5 Volume calculation

14.6 Discussion

References

Index.

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