Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

<p>Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sou...

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Bibliographic Details
Main Author: Han, Shaobo
Other Authors: Carin, Lawrence
Published: 2016
Subjects:
Artificial intelligence
Statistics
Computer science
Bayesian
Copula
Dynamic Modeling
Machine Learning
Transfer Learning
Variational Inference
Online Access:http://hdl.handle.net/10161/12828

Internet

http://hdl.handle.net/10161/12828

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