Efficient Multiscale Sampling from Products of Gaussian Mixtures

By A. Ihler, Erik B. Sudderth, William T. Freeman, Alan S. Willsky. 2003

106 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Le Song
2. Arthur Gretton
3. Kenji Fukumizu
4. Krikamol Muandet
5. Bernhard Schölkopf
6. Bo Dai
7. Yoshua Bengio
8. Carlos Guestrin

Citations in the corpus

• Kernel Belief Propagation (2011, methodology): “In our implementation, we used the more efficient multiscale KD-tree sampling method of Ihler et al. (2003).”
• Fault Identification Via Nonparametric Belief Propagation (2009, background): “A number of sampling algorithms have been designed to ensure that sampling is efficient [43]–[45].”
• Nonparametric belief propagation for self-localization of sensor networks (2005, methodology): “However, efficient methods of drawing samples from the product of several Gaussian mixture densities are investigated in [25]; in this work, we primarily use a technique called mixture importance sampling.”