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Found 3 papers, 1 papers with code
Computing Marginal and Conditional Divergences between Decomposable Models with Applications
Doing so tractably is non-trivial as we need to decompose the divergence between these distributions and therefore, require a decomposition over the marginal and conditional distributions of these models.
Computing Divergences between Discrete Decomposable Models
We show that we are able to compute a wide family of functionals and divergences, such as the alpha-beta divergence, between two decomposable models, i. e. chordal Markov networks, in time exponential to the treewidth of these models.
Understanding Concept Drift
Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning.
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