Gradient Importance Sampling
By Ingmar Schuster. 2015
28 citations recorded by the source corpus.
Adaptive Monte Carlo schemes developed over the last years usually seek to ensure ergodicity of the sampling process in line with MCMC tradition. This poses constraints on what is possible in terms of adaptation. In the general case ergodicity can only be guaranteed if adaptation is diminished at a certain rate. Importance Sampling approaches offer a way to circumvent this limitation and design sampling algorithms that keep adapting. Here I present a gradient informed variant of SMC (and its special case Population Monte Carlo) for static problems.
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
- Heiko Strathmann
- Dino Sejdinovic
- Brooks Paige
- Ingmar Schuster
- Arthur Gretton
- Bernhard Schölkopf
- Bharath K. Sriperumbudur
- Frank D. Wood
In Statistics and Probability
- Heiko Strathmann
- Dino Sejdinovic
- Brooks Paige
- Ingmar Schuster
- Arthur Gretton
- Bharath K. Sriperumbudur
- Bernhard Schölkopf
- Kenji Fukumizu
Citations in the corpus
- Kernel Sequential Monte Carlo (2015, methodology): “We arrive at kernel GRIS, a gradient-free variant of GRIS [19].”