"Ideal Parent" Structure Learning for Continuous Variable Networks

In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.

• Publication year

  2004

• Venue

  Conference on Uncertainty in Artificial Intelligence

• Publication date

  2004-07-07

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 1207.4133
• 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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