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View all- Xuan JLu JZhang G(2019)A Survey on Bayesian Nonparametric LearningACM Computing Surveys10.1145/329104452:1(1-36)Online publication date: 25-Jan-2019
The beta processes (BP) is a powerful nonparametric tool in feature learning, which is often used as the prior of Bernoulli process for choosing features from a feature dictionary. However, it still shows a limitation in processing some real-world ...
The number of states in a hidden Markov model (HMM) is an Important parameter that has a critical impact on the inferred model. Bayesian approaches to addressing this issue include the nonparametric hierarchical Dirichlet process, which does not extend ...
It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation ...
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