Abstract
Adaptive importance sampling using kernel density estimation techniques was introduced by West. This technique adapts the importance sampling function to the underlying integrand, thus yielding small-variance estimates. One drawback of this approach is that evaluation of the kernel mixture density is slow. We present a linear tensor spline representation of the adaptive importance function using variable bandwidth kernels that retains the small variance properties of West’s approach but executes more quickly.
Original language | English (US) |
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Pages (from-to) | 158-174 |
Number of pages | 17 |
Journal | Journal of Computational and Graphical Statistics |
Volume | 7 |
Issue number | 2 |
DOIs | |
State | Published - Jun 1998 |
Keywords
- Bayesian computing
- Kernel density estimation
- Splines