Editorial: recent developments in mixture models

By Dankmar Böhning, Wilfried Seidel. 2003

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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

1. Bernhard Schölkopf
2. Dominik Janzing
3. Jan Peters
4. Kun Zhang
5. David Lopez-Paz
6. Eleni Sgouritsa
7. Yoshua Bengio
8. Sergey Levine

In Statistics and Probability

1. Bernhard Schölkopf
2. Dominik Janzing
3. Jan Peters
4. Eleni Sgouritsa
5. David Lopez-Paz
6. Peter Buhlmann
7. Mi-Ja Woo
8. T. Sriram

Citations in the corpus

• Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders (2013, methodology): “Probabilistic mixture models or other clustering meth-ods can be used to identify the mixture components (clusters) (e.g., Von Luxburg (2007); B¨ohning & Seidel (2003); Rasmussen (2000); Iwata et al. (2013)).”
• Robust Estimation of Mixture Complexity (2006, methodology): “Comprehensive accounts of statistical inference for mixture models with applications have been given by Everitt and Hand (1981), Titterington, Smith, and Makov (1985), McLachlan and Basford (1988), Lindsay (1995), Böhning (1999), McLachlan and Peel (2000), and Böhning and Seidel (2003).”