Latent Tree Models and Approximate Inference in Bayesian Networks

Understand

We propose a novel method for approximate inference in Bayesian networks (BNs).

• The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and when online, make inference with the LTM instead of the original BN.
• Because LTMs are tree-structured, inference takes linear time.
• In the meantime, they can represent complex relationship among leaf nodes and hence the approximation accuracy is often good.