Hierarchical Bayesian Network

A Hierarchical Bayesian Network is a Bayesian network in the form of a directed tree structure.

• AKA: Hierarchical Bayesian Model.
• Context:
  • It can be associated to a Hierarchical Bayesian Metamodel.
  • It can be an input to a Hierarchical Bayesian Modeling Task (solved by a hierarchical Bayesian modeling system).
• Example(s):
  • http://www.springerimages.com/img/Images/BMC/MEDIUM_1471-2105-11-S3-S4-1.jpg “Hierarchical Bayesian network developed to predict TB major lineages using spoligotypes and MIRU.”
• Counter-Example(s):
  • a General Bayesian Network, such as http://cybergeo.revues.org/docannexe/image/2777/img-7-small487.png
  • a Hierarchical Bayesian Metamodel, such as http://en.wikipedia.org/wiki/File:Latent_Dirichlet_allocation.svg
• See: Hierarchical Model, Bayesian Network.

---

---

References

2009

• (Wang et al., 2009) ⇒ Xiaogang Wang, Xiaoxu Ma, and W. E. L. Grimson. (2009). “Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models.” In: IEEE Transactions on Pattern Analysis and Machine Intelligence Journal, 31(3). doi:10.1109/TPAMI.2008.87
  • QUOTE: We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Hierarchical Bayesian models are used to connect three elements in visual surveillance: low-level visual features, simple "atomic" activities, and interactions. … In this paper, we propose three hierarchical Bayesian models, Latent Dirichlet Allocation (LDA) mixture model, Hierarchical Dirichlet Process (HDP) mixture model, and Dual Hierarchical Dirichlet Processes (Dual-HDP) model.

---