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Research Frontiers in Bayesian Nonparametric Models

Development of Bayesian inference methods that avoid specifying finite-dimensional parameter spaces using techniques like Dirichlet processes.

Infinite Mixture Models in High-Dimensional Data
Dirichlet Process Priors for Unknown Clustering Structures
Nonparametric Bayesian Methods in Functional Data Analysis
Stick-Breaking Priors and Adaptive Partitioning
Gaussian Process Regression Beyond Parametric Boundaries
Chinese Restaurant Process Applications in Network Inference
Dependent Nonparametric Priors for Multivariate Modeling
Hierarchical Dirichlet Processes in Complex Data Structures
Polya Urn Models and Sequential Bayesian Learning
Nonparametric Shape Constraints in Density Estimation

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