Mixed Cumulative Distribution Networks
By Ricardo M. A. Silva, Charles Blundell, Yee Whye Teh. 2010
16 citations recorded by the source corpus.
Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly. Unfortunately there are currently no good parameterizations of general ADMGs. In this paper, we apply recent work on cumulative distribution networks and copulas to propose one one general construction for ADMG models. We consider a simple parameter estimation approach, and report some encouraging experimental results.
Subfields: Artificial Intelligence
How researchers see this paper
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In Artificial Intelligence
- Yoshua Bengio
- Benigno Uria
- Ruslan Salakhutdinov
- Yichuan Tang
- Kyunghyun Cho
- Quoc V. Le
- Mohit Bansal
- Geoffrey E. Hinton
Citations in the corpus
- Deep Mixtures of Factor Analysers (2012, methodology): “Following (Silva et al., 2011), we used 4 continuous datasets from the UCI repository.”
- Connectionist multivariate density-estimation and its application to speech synthesis (2016, methodology): “1 Low-dimensional data We first considered three UCI datasets (Bache and Lichman, 2013), previously used to study the performance of other density estimators (Silva et al., 2011; Tang et al., 2012), namely: red wine, white wine and parkinsons.”
- Copulas in Machine Learning (2013, background): “Also of great interest but not presented here is the use of copulas as a particular instance within the cumulative distribution network model [17, 45].”