Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis
By Michael E. Tipping, Neil D. Lawrence. 2005
104 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
In Artificial Intelligence
- David M. Blei
- Andrew Gelman
- Aki Vehtari
- Yuling Yao
- Alp Kucukelbir
- Jon D. McAuliffe
- Stephan Mandt
- Matthew D. Hoffman
In Signal Processing
- Jason A. Palmer
- Kenneth Kreutz-Delgado
- Andrew Gelman
- David M. Blei
- Scott Makeig
- Alp Kucukelbir
- Aki Vehtari
- Yuling Yao
Citations in the corpus
- Variational Inference: A Review for Statisticians (2016, background): “…models (Roberts et al. 2004; Barber and Chiappa 2006; Archambeau et al. 2007a, 2007b; Johnson and Willsky 2014; Foti et al. 2014), robust models (Tipping and Lawrence 2005; Wang and Blei 2015), and Gaussian process models (Titsias and Lawrence 2010; Damianou, Titsias, and Lawrence 2011;…”
- Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors (2020, methodology): “Approximate methods exist, such as factorizing variational approximation (Tipping and Lawrence, 2005),”
- AMICA : An Adaptive Mixture of Independent Component Analyzers with Shared Components (2011, background): “Non-Gaussian sources in the form of Gaussian scale mixtures, in particular Student’s t distribution, were developed in [4]–[6].”