Using Causal Information and Local Measures to Learn Bayesian Networks
By Wai Lam, Fahiem Bacchus. 1993
In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeoff, in a principled way, the accuracy of the learned network against its practical usefulness. In this paper we present some new results that have arisen from our work. In particular, we present a new local way of computing the description length. This allows us to make significant improvements in our search algorithm. In addition, we modify our algorithm so that it can take into account partial domain information that might be provided by a domain expert. The local computation of description length also opens the door for local refinement of an existent network. The feasibility of our approach is demonstrated by experiments involving networks of a practical size.
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- Anirudh Goyal
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Citations in the corpus
- A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms (2019, methodology): “There, it has been common to use Minimum Description Length (MDL) principles to score and search over models Lam and Bacchus (1993); Friedman and Goldszmidt (1998), or the Bayesian Information Criterion (BIC) to search for models with high relative posterior probability Heckerman et al. (1995).”
- Learning Bayesian networks: approaches and issues (2011, methodology): “Finally, Liu et al. (2007a) learn structures using a CI testing method and then combine the resulting DAGs into a single DAG.”