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Produktbild: Mixtures
Band 887

Mixtures Estimation and Applications

Aus der Reihe Wiley Series in Probability and Statistics

Fr. 132.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Verlag

John Wiley & Sons Inc

Seitenzahl

330

Maße (L/B/H)

2.3/15.2/22.9 cm

Gewicht

628 g

Sprache

Englisch

ISBN

978-1-119-99389-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Verlag

John Wiley & Sons Inc

Seitenzahl

330

Maße (L/B/H)

2.3/15.2/22.9 cm

Gewicht

628 g

Sprache

Englisch

ISBN

978-1-119-99389-6

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  • Produktbild: Mixtures
  • PrefaceAcknowledgementsList of Contributors1 The EM algorithm, variational approximations and expectation propagation for mixturesD.Michael Titterington1.1 Preamble1.2 The EM algorithm1.3 Variational approximations1.4 Expectation-propagationAcknowledgementsReferences2 Online expectation maximisationOlivier Cappé2.1 Introduction2.2 Model and assumptions2.3 The EM algorithm and the limiting EM recursion2.4 Online expectation maximisation2.5 DiscussionReferences3 The limiting distribution of the EM test of the order of a finite mixtureJ. Chen and Pengfei Li3.1 Introduction3.2 The method and theory of the EM test3.3 Proofs3.4 DiscussionReferences4 Comparing Wald and likelihood regions applied to locally identifiable mixture modelsDaeyoung Kim and Bruce G. Lindsay4.1 Introduction4.2 Background on likelihood confidence regions4.3 Background on simulation and visualisation of the likelihood regions4.4 Comparison between the likelihood regions and the Wald regions4.5 Application to a finite mixture model4.6 Data analysis4.7 DiscussionReferences5 Mixture of experts modelling with social science applicationsIsobel Claire Gormley and Thomas Brendan Murphy5.1 Introduction5.2 Motivating examples5.3 Mixture models5.4 Mixture of experts models5.5 A Mixture of experts model for ranked preference data5.6 A Mixture of experts latent position cluster model5.7 DiscussionAcknowledgementsReferences6 Modelling conditional densities using finite smooth mixturesFeng Li, Mattias Villani and Robert Kohn6.1 Introduction6.2 The model and prior6.3 Inference methodology6.4 Applications6.5 ConclusionsAcknowledgementsAppendix: Implementation details for the gamma and log-normal modelsReferences7 Nonparametric mixed membership modelling using the IBP compound Dirichlet processSinead Williamson, Chong Wang, Katherine A. Heller, and David M. Blei7.1 Introduction7.2 Mixed membership models7.3 Motivation7.4 Decorrelating prevalence and proportion7.5 Related models7.6 Empirical studies7.7 DiscussionReferences8 Discovering nonbinary hierarchical structures with Bayesian rose treesCharles Blundell, Yee Whye Teh, and Katherine A. Heller8.1 Introduction8.2 Prior work8.3 Rose trees, partitions and mixtures8.4 Greedy Construction of Bayesian Rose Tree Mixtures8.5 Bayesian hierarchical clustering, Dirichlet process models and product partition models8.6 Results8.7 DiscussionReferences9 Mixtures of factor analyzers for the analysis of high-dimensional dataGeoffrey J. McLachlan, Jangsun Baek, and Suren I. Rathnayake9.1 Introduction9.2 Single-factor analysis model9.3 Mixtures of factor analyzers9.4 Mixtures of common factor analyzers (MCFA)9.5 Some related approaches9.6 Fitting of factor-analytic models9.7 Choice of the number of factors q9.8 Example9.9 Low-dimensional plots via MCFA approach9.10 Multivariate t-factor analysers9.11 DiscussionAppendixReferences10 Dealing with Label Switching under model uncertaintySylvia Frühwirth-Schnatter10.1 Introduction10.2 Labelling through clustering in the point-process representation10.3 Identifying mixtures when the number of components is unknown10.4 Overfitting heterogeneity of component-specific parameters10.5 Concluding remarksReferences11 Exact Bayesian analysis of mixturesChristian .P. Robert and Kerrie L. Mengersen11.1 Introduction11.2 Formal derivation of the posterior distributionReferences12 Manifold MCMC for mixturesVassilios Stathopoulos and Mark Girolami12.1 Introduction12.2 Markov chain Monte Carlo methods12.3 Finite Gaussian mixture models12.4 Experiments12.5 DiscussionAcknowledgementsAppendixReferences13 How many components in a finite mixture?Murray Aitkin13.1 Introduction13.2 The galaxy data13.3 The normal mixture model13.4 Bayesian analyses13.5 Posterior distributions for K (for flat prior)13.6 Conclusions from the Bayesian analyses13.7 Posterior distributions of the model deviances13.8 Asymptotic distributions13.9 Posterior deviances for the galaxy data13.10 ConclusionReferences14 Bayesian mixture models: a blood-free dissection of a sheepClair L. Alston, Kerrie L. Mengersen, and Graham E. Gardner14.1 Introduction14.2 Mixture models14.3 Altering dimensions of the mixture model14.4 Bayesian mixture model incorporating spatial information14.5 Volume calculation14.6 DiscussionReferencesIndex.