Handbook of Mixture Analysis

Table of Contents: “…4.3.3 Boundary parameters: overfitted mixtures4.3.4 Asymptotic behaviour of posterior estimates of the number of components; 4.4 Concluding Remarks; Bibliography; 5: Computational Solutions for Bayesian Inference in Mixture Models; 5.1 Introduction; 5.2 Algorithms for Posterior Sampling; 5.2.1 A computational problem? Which computational problem?; 5.2.2 Gibbs sampling; 5.2.3 Metropolis-Hastings schemes; 5.2.4 Reversible jump MCMC; 5.2.5 Sequential Monte Carlo; 5.2.6 Nested sampling; 5.3 Bayesian Inference in the Model-Based Clustering Context; 5.4 Simulation Studies…”
Taylor & Francis
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Handbook of Mixture Analysis

Table of Contents: “…4.3.3 Boundary parameters: overfitted mixtures4.3.4 Asymptotic behaviour of posterior estimates of the number of components; 4.4 Concluding Remarks; Bibliography; 5: Computational Solutions for Bayesian Inference in Mixture Models; 5.1 Introduction; 5.2 Algorithms for Posterior Sampling; 5.2.1 A computational problem? Which computational problem?; 5.2.2 Gibbs sampling; 5.2.3 Metropolis-Hastings schemes; 5.2.4 Reversible jump MCMC; 5.2.5 Sequential Monte Carlo; 5.2.6 Nested sampling; 5.3 Bayesian Inference in the Model-Based Clustering Context; 5.4 Simulation Studies…”
Taylor & Francis
OCLC metadata license agreement