Mixture density estimation via Hilbert space embedding of measures

By Bharath K. Sriperumbudur. 2011

9 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics, 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 Control and Systems Engineering

1. Arthur Gretton
2. Bernhard Schölkopf
3. Dino Sejdinovic
4. Bharath K. Sriperumbudur
5. Kenji Fukumizu
6. Krikamol Muandet
7. Ilya O. Tolstikhin
8. Gert R. G. Lanckriet

In Statistical and Nonlinear Physics

1. Arthur Gretton
2. Bernhard Schölkopf
3. Bharath K. Sriperumbudur
4. Kenji Fukumizu
5. Krikamol Muandet
6. Dino Sejdinovic
7. Ilya O. Tolstikhin
8. Gert R. G. Lanckriet

In Statistics and Probability

1. Arthur Gretton
2. Bharath K. Sriperumbudur
3. Bernhard Schölkopf
4. Kenji Fukumizu
5. Krikamol Muandet
6. Dino Sejdinovic
7. Gert R. G. Lanckriet
8. Kacper P. Chwialkowski

In Probabilistic & Bayesian Methods

1. Bharath K. Sriperumbudur
2. Kenji Fukumizu
3. Arthur Gretton
4. Bernhard Schölkopf
5. Krikamol Muandet
6. Dino Sejdinovic
7. Ilya O. Tolstikhin
8. Gert R. G. Lanckriet

Citations in the corpus

• Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “Sriperumbudur et al. (2012) provides a thorough discussion on several properties of this estimator.”
• Minimax Estimation of Kernel Mean Embeddings (2016, background): “…and conditional independence tests (Gretton et al., 2008; Fukumizu et al., 2008), covariate-shift (Smola et al., 2007), density estimation (Sriperumbudur, 2011), feature selection (Song et al., 2012), causal inference (Lopez-Paz et al., 2015), kernel Bayes’ rule (Fukumizu et al., 2013)…”
• Hypothesis testing using pairwise distances and associated kernels (2012, methodology): “…the associated kernels may be used much more widely: in conditional dependence testing and estimates of the chi-squared distance (Fukumizu et al., 2008), in Bayesian inference (Fukumizu et al., 2011), in mixture density estimation (Sriperumbudur, 2011) and in other machine learning applications.”
• On the empirical estimation of integral probability metrics (2012, methodology): “The kernel distance is used in statistical applications including homogeneity testing [20, 21], independence testing [22], testing for conditional independence [17] and mixture density estimation [42].”