Fitting Mixtures of Kent Distributions to Aid in Joint Set Identification

By David Peel, William J. Whiten, Geoffrey J. McLachlan. 2001

94 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mechanical Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ae27275b88d87914c29968a906d771083a8bab9b

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. Adrian E. Raftery
2. Chris Fraley
3. Yee Whye Teh
4. Arnaud Doucet
5. David M. Blei
6. Daniel Hernandez-Stumpfhauser
7. James Thornton
8. F. J. Breidt

In Mechanical Engineering

1. Daniel Hernandez-Stumpfhauser
2. F. J. Breidt
3. M. J. van der Woerd

In Statistics and Probability

1. Adrian E. Raftery
2. Chris Fraley
3. Yee Whye Teh
4. Arnaud Doucet
5. James Thornton
6. Valentin De Bortoli
7. Emile Mathieu
8. Daniel Hernandez-Stumpfhauser

Citations in the corpus

• The General Projected Normal Distribution of Arbitrary Dimension: Modeling and Bayesian Inference (2017, methodology): “, 2005) or Kent distributions (Peel et al., 2001), which are both unimodal distributions.”
• Model-Based Clustering, Discriminant Analysis, and Density Estimation (2002, background): “…in clustering and related contexts include mixtures of t distributions (Peel and McLachlan 2000), mixtures of trees (Meila 1999), mixtures of rst-order Markov chains (Cadez, Heckerman, Meek, Smyth, and White 2000), and mixtures of distributions for angular data (Peel, Whitten, and McLachlan 2001).”
• Riemannian Score-Based Generative Modeling (2022, methodology): “We compare our approach to three previous baselines: a mixture of Kent distributions (Peel et al., 2001), Riemannian Continuous Normalizing Flows (Mathieu and Nickel, 2020), and Moser Flows (Rozen et al., 2021).”