Adaptive Importance Sampling from Probabilistic Tree Automata

摘要

We present a general importance sampling technique for approximating expected values based on samples from probabilistic finite tree automata. The algorithm uses the samples it produces to adapt rule probabilities for the automaton in order to improve sample quality.