UNCERTAINTY IN ARTIFICIAL INTELLIGENCE PROCEEDINGS 2000 Tractable Bayesian Learning of Tree Belief Networks

By Marina Meila, Tommi S. Jaakkola. 2011

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

1. Lukas Burger
2. Andreas Krause
3. Michael R. Siracusa
4. Bernhard Schölkopf
5. John W. Fisher III
6. Jonas Rothfuss
7. Lars Lorch
8. E. van Nimwegen

Citations in the corpus

• Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments (2010, methodology): “The sum over spanning trees in (9) can be calculated using a generalization of Kirchhoff’s matrix-tree theorem [28].”
• DiBS: Differentiable Bayesian Structure Learning (2021, background): “, for tree structures or known node orderings, exact inference can be performed more efficiently [45, 46].”
• Tractable Bayesian Inference of Time-Series Dependence Structure (2009, methodology): “A proof follows that of (Meila and Jaakkola, 2006) substituting in the directed tree partition functionin place of the undirected version.”