信度网络推理的最优Monte Carlo估计
Optimal Monte Carlo Estimation of Belief Network Inference
摘要：
我们提出了两个蒙特卡罗抽样算法的概率推理，保证多项式时间收敛的一个更大的类的网络比目前的抽样算法提供。这些新方法是已知的似然加权算法的变体。我们使用最佳停止规则理论的最新进展，蒙特卡罗模拟，以获得一个推理近似的相对误差和一个小的失败概率δ。我们提出了一个实证评估的算法，证明其改进的性能。
英文摘要：
We present two Monte Carlo sampling algorithms for probabilistic inference that guarantee polynomial-time convergence for a larger class of network than current sampling algorithms provide. These new methods are variants of the known likelihood weighting algorithm. We use of recent advances in the theory of optimal stopping rules for Monte Carlo simulation to obtain an inference approximation with relative error epsilon and a small failure probability delta. We present an empirical evaluation of the algorithms which demonstrates their improved performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
