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Bayesian Mixture of Latent Class Analysis Models with the Telescoping Sampler6 months ago
Introduction | Data | Model specification | Specification of the MCMC simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimate $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws | Step 4: Estimating data cluster specific parameters and determining the final partition | Step 4: Visualizing the posterior component occurrence and precision distributions mu_k and phi_j. | References
telescope 0.2-2Gertraud Malsiner-Walli, Bettina Grün, Sylvia Frühwirth-SchnatterBayesian_LCA_mixtures.Rmd
Using CliPS to Identify a Dynamic Mixture of Finite Mixtures of Multivariate Gaussian Distributions -- Case Study: Diabetes Data6 months ago
Introduction | Step 1: Define a mixture model | Step 2: MCMC sampling from the posterior | Step 2b: Post-processing the MCMC draws | Steps 3-4-5: Clustering of the draws | Step 6: Characterization of the cluster distributions | References
telescope 0.2-2Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina GrünCliPS_diabetes.Rmd
Bayesian Latent Class Analysis Models with the Telescoping Sampler2 years ago
Introduction | The fear data set | Model specification | Specification of the MCMC simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimate $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws | Step 4: Estimating data cluster specific parameters and determining the final partition | References
telescope 0.2-2Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina GrünBayesian_LCA.Rmd
Bayesian Multivariate Gaussian Mixtures with the Telescoping Sampler2 years ago
Introduction | The thyroid data set | Model specification | Specification of the simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimating $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws in the point process representation | Step 4: Estimating data cluster specific parameters and determining the final partition | Step 5: Visualizing the estimated classification | References
telescope 0.2-2Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina GrünBayesian_mult_Gaussian_mixtures.Rmd
Bayesian Poisson Mixtures with the Telescoping Sampler2 years ago
Introduction | The eye data set | Model specification | Specification of the simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimate $K_+$ and $K$ | Step 2: Extract those draws which correspond to samples with exactly $\hat{K}_+$ filled components | Step 3: Clustering and relabeling of the MCMC draws, with $\hat{K}_+$ being the estimated number of data clusters | References
telescope 0.2-2Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina GrünBayesian_Poisson_mixtures.Rmd
Bayesian Univariate Gaussian Mixtures with the Telescoping Sampler2 years ago
Introduction | The Galaxy data set | Model specification | Specification of the MCMC simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimating $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws in the point process representation | Step 4: Estimating data cluster specific parameters and determing the final partition | Step 5: Visualizing the estimated classification | References
telescope 0.2-2Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina GrünBayesian_univ_Gaussian_mixtures.Rmd