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SAFE ADAPTIVE IMPORTANCE SAMPLING: A MIXTURE APPROACH
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AI
DOI：10.1214/20-AOS1983.png)
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Abstract
En 中文
This paper investigates adaptive importance sampling algorithms for which the policy, the sequence of distributions used to generate the particles, is a mixture distribution between a flexible kernel density estimate (based on the previous particles), and a safe heavy-tailed density. When the share of samples generated according to the safe density goes to zero but not too quickly, two results are established: (i) uniform convergence rates are derived for the policy toward the target density; (ii) a central limit theorem is obtained for the resulting integral estimates. The fact that the asymptotic variance is the same as the variance of an oracle procedure with variance-optimal policy, illustrates the benefits of the approach. In addition, a subsampling step (among the particles) can be conducted before constructing the kernel estimate in order to decrease the computational effort without altering the performance of the method. The practical behavior of the algorithms is illustrated in a simulation study.
Keywords:
Monte Carlo methods
adaptive importance sampling
kernel density estimation
martingale methods
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