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HDP

Hierarchical Dirichlet Process (HDP) is a non-parametric Bayesian technique widely utilized in machine learning and data analysis. It introduces an infinite mixture model where each data point is associated with a mixture component, and these components are drawn from a shared, infinite pool of potential mixture components. HDP allows for potential growth in the number of mixture components as the volume of data increases. This approach enables better flexibility and modeling capabilities. It has found applications in various fields such as natural language processing, image processing, and bioinformatics.