具有确定性因果关系的贝叶斯网络随机抽样的难度度量：k检验
Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test
摘要：
近似贝叶斯推理是NP难的。Dagum和Luby定义了局部方差界（Local Variance Bound，LVB）来衡量贝叶斯网络上贝叶斯推理的近似硬度，假设网络模型严格为正的联合概率分布，即不允许零概率。本文介绍了k-检验来衡量贝叶斯网络上的推理的近似硬度与确定性的因果关系的概率分布，即当零条件概率是允许的。通过随机采样的近似是一种广泛使用的推断方法，已知该方法由于样本拒绝而效率低下。k检验预测贝叶斯网络随机抽样的拒绝率何时会低，适度，高，或者当抽样是棘手的。
英文摘要：
Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Local Variance Bound (LVB) to measure the approximation hardness of Bayesian inference on Bayesian networks, assuming the networks model strictly positive joint probability distributions, i.e. zero probabilities are not permitted. This paper introduces the k-test to measure the approximation hardness of inference on Bayesian networks with deterministic causalities in the probability distribution, i.e. when zero conditional probabilities are permitted. Approximation by stochastic sampling is a widely-used inference method that is known to suffer from inefficiencies due to sample rejection. The k-test predicts when rejection rates of stochastic sampling a Bayesian network will be low, modest, high, or when sampling is intractable.
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