Bayesian Nonparametric Models: 
Applications and Extensions in Complex Data Modeling

Jingyao Wang

doi:10.70711/memf.v3i7.9480

Bayesian Nonparametric Models: Applications and Extensions in Complex Data Modeling

Jingyao Wang

Abstract

The large-scale emergence of complex data (high-dimensional, heterogeneous, dynamic, and sparse) in fields such as finance, biomedicine, and artificial intelligence poses severe challenges to the preset structures and fixed-dimensional assumptions of traditional parametric models. Bayesian nonparametric models, leveraging their core advantages of "adaptive parameter dimensionality, " "strong prior flexibility,
" and "accurate uncertainty quantification, " have become a key tool for complex data modeling.

Keywords

Bayesian nonparametric models; Complex data modeling; Dirichlet Process; Indian Buffet Process; Gaussian Process; Model extension; High-dimensional data; Heterogeneous data

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DOI: http://dx.doi.org/10.70711/memf.v3i7.9480

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