Truncated importance sampling
Truncated importance sampling
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DOI：
10.1198/106186008x320456
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发表时间：
2008-06-01
影响因子：
2.4
通讯作者：
Ionides, Edward L.
中科院分区：
文献类型：
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作者：
Ionides, Edward L.
重要性抽样是一种基本的蒙特卡罗技术。它涉及从建议分布中生成样本，以估计目标分布的某些属性。重要性抽样可以是高度敏感的建议分布的选择，并失败，如果建议的分布没有足够好地近似的目标，程序，涉及截断大的重要性抽样权重理论上被证明是不太敏感的建议分布，并具有较低的均方估计误差，以提高标准的重要性抽样。弱条件下的一致性，并在更具体的条件下找到最佳截断率。截断率n（1/2）是一个很好的一般选择。一个自适应截断阈值，最小化无偏风险估计的基础上，也提出了。作为一个例子，截断被发现是有效的计算部分观察到的多元扩散的可能性。它被证明是连续时间人口疾病模型的序贯重要性抽样方案的一个组成部分。截断对于计算密集的多维情况最有价值，在这种情况下，找到一个处处都是目标分布的良好近似的建议分布是具有挑战性的。
importance sampling is a fundamental Monte Carlo technique. It involves generating a sample from a proposal distribution in order to estimate some property of a target distribution. Importance sampling can be highly sensitive to the choice of proposal distribution, and fails if the proposal distribution does not sufficiently well approximate the target, Procedures that involve truncation of large importance sampling weights are shown theoretically to improve on standard importance sampling by being less sensitive to the proposal distribution and having lower mean squared estimation error. Consistency is shown under weak conditions, and optimal truncation rates found under more specific conditions. Truncation at rate n(1/2) is shown to be a good general choice. An adaptive truncation threshold, based on minimizing an unbiased risk estimate, is also presented. As an example, truncation is found to be effective for calculating the likelihood of partially observed multivariate diffusions. It is demonstrated as a component of a sequential importance sampling scheme for a continuous time population disease model. Truncation is most valuable for computationally intensive, multidimensional situations in which finding a proposal distribution that is everywhere a good approximation to the target distribution is challenging.