60 citations · 168 across the 6 of their papers we have counts for
6 papers
When do Numbers Really Matter?
Hei Chan, Adnan Darwiche
Common wisdom has it that small distinctions in the probabilities quantifying a Bayesian network do not matter much for the resultsof probabilistic queries. However, one can easily…
Reasoning about Bayesian Network Classifiers
Hei Chan, Adnan Darwiche
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about B…
Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
Hei Chan, Adnan Darwiche
Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes,…
On Bayesian Network Approximation by Edge Deletion
Arthur Choi, Hei Chan, Adnan Darwiche
We consider the problem of deleting edges from a Bayesian network for the purpose of simplifying models in probabilistic inference. In particular, we propose a new method for delet…
On the Robustness of Most Probable Explanations
Hei Chan, Adnan Darwiche
In Bayesian networks, a Most Probable Explanation (MPE) is a complete variable instantiation with a highest probability given the current evidence. In this paper, we discuss the pr…
When do Numbers Really Matter?
H. Chan, A. Darwiche
Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one ca…