Latent Tree Models and Approximate Inference in Bayesian Networks

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. Empirical evidence shows that our method can achieve good approximation accuracy at low online computational cost.

• Publication year

  2008

• Venue

  AAAI Conference on Artificial Intelligence

• Publication date

  2008-05-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1613/jair.2530arXiv 1401.3429
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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