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Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models
This allows us to study the performances of popular Metropolis-within-Gibbs schemes for non-conjugate hierarchical models, in high-dimensional regimes where both number of datapoints and parameters increase.
Dimension-free mixing times of Gibbs samplers for Bayesian hierarchical models
Gibbs samplers are popular algorithms to approximate posterior distributions arising from Bayesian hierarchical models.
Clustering consistency with Dirichlet process mixtures
Dirichlet process mixtures are flexible non-parametric models, particularly suited to density estimation and probabilistic clustering.
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