  Improving efficiency of importance sampler is at the center of research in
Monte Carlo methods. While adaptive approach is usually difficult within the
Markov Chain Monte Carlo framework, the counterpart in importance sampling can
be justified and validated easily. We propose an iterative adaptation method
for learning the proposal distribution of an importance sampler based on
stochastic approximation. The stochastic approximation method can recruit
general iterative optimization techniques like the minorization-maximization
algorithm. The effectiveness of the approach in optimizing the Kullback
divergence between the proposal distribution and the target is demonstrated
using several simple examples.
