Permutation Testing Improves Bayesian Network Learning
By Ioannis Tsamardinos, Giorgos Borboudakis. 2010
55 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability, 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 Artificial Intelligence
- Bernhard Schölkopf
- Krikamol Muandet
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Arthur Gretton
- David Lopez-Paz
- Kun Zhang
- Dino Sejdinovic
In Statistics and Probability
- Bernhard Schölkopf
- Krikamol Muandet
- Kenji Fukumizu
- Bharath K. Sriperumbudur
- Arthur Gretton
- Kun Zhang
- Dino Sejdinovic
- Gary Doran
In Probabilistic & Bayesian Methods
- Bernhard Schölkopf
- Krikamol Muandet
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Arthur Gretton
- Kun Zhang
- Gary Doran
- Dino Sejdinovic
Citations in the corpus
- Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “104 Hilbert Space Embedding of Conditional Distributions There exist numerous tests for conditional dependency such as partial correlation tests (Baba et al. 2004), conditional densities based tests (Su and White 2008), and permutation-based tests (Tsamardinos and Borboudakis 2010).”
- Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery (2017, background): “Another permutation-based test generalizes the same strategy even when X and Y are not necessarily discrete [5].”
- A Permutation-Based Kernel Conditional Independence Test (2014, methodology): “Our results align with those observed in prior work, in which permutation-based conditional independence tests for datasets with discrete values were found to be well-calibrated with respect to asymptotic tests (Tsamardinos and Borboudakis, 2010).”