Based on an analogy between thermodynamics and Bayesian inference,inverse halftoning was formulated using multiple halftone images based on Bayesian inference using the maximizer of the posterior marginal(MPM) estimat...Based on an analogy between thermodynamics and Bayesian inference,inverse halftoning was formulated using multiple halftone images based on Bayesian inference using the maximizer of the posterior marginal(MPM) estimate.Applying Monte Carlo simulation to a set of snapshots of the Q-Ising model,it was demonstrated that optimal performance is achieved around the Bayes-optimal condition within statistical uncertainty and that the performance of the Bayes-optimal solution is superior to that of the maximum-a-posteriori(MAP) estimation which is a deterministic limit of the MPM estimate.These properties were qualitatively confirmed by the mean-field theory using an infinite-range model established in statistical mechanics.Additionally,a practical and useful method was constructed using the statistical mechanical iterative method via the Bethe approximation.Numerical simulations for a 256-grayscale standard image show that Bethe approximation works as good as the MPM estimation if the parameters are set appropriately.展开更多
文摘目的:探讨胎盘生长因子(placental growth factor,PLGF)、可溶性fms样酪氨酸激酶-1(soluble fms-like tyrosine kinase-1,SFLT-1)和糖基化纤连蛋白(glycosylated fibronectin,GLYFN)检测对子痫前期的预测价值。方法:选择在无锡市妇幼保健院就诊的188例孕妇,分154例正常孕妇(对照组)和34例子痫前期患者(子痫组),应用免疫荧光法分别检测其在孕16~18周血清中PLGF、SFLT-1和GLYFN的浓度,比较子痫前期组和对照组各标志物的水平,并使用受试者操作特征曲线(receiver operating characteristic,ROC)对3种标志物的预测价值进行效能评估。结果:在妊娠中期,子痫前期组血清PLGF浓度低于对照组,SFLT-1及GLYFN浓度均高于对照组,3种标志物的差异均有统计学意义(3指标P=0.000)。95%置信区间的ROC曲线下面积(areas under the ROC curve,AUC)为,PLGF为0.941(0.907~0.974),SFLT-1为0.881(0.800~0.962),GLYFN为0.951(0.918~0.985),联合指标SFLT-1和GLYFN、3项指标联合检测在ROC曲线下面积(areas under the ROC curve,AUC)分别为0.968、0.986。结论:PLGF、SFLT-1、GLYFN 3种标志物水平在对照组和子痫前期组均存在明显差异,对子痫前期的发病具有一定的预测价值,SFLT-1联合PLGF、SFLT-1联合GLYFN、3项指标联合检测对子痫前期的预测价值高于任一单项指标。
文摘Based on an analogy between thermodynamics and Bayesian inference,inverse halftoning was formulated using multiple halftone images based on Bayesian inference using the maximizer of the posterior marginal(MPM) estimate.Applying Monte Carlo simulation to a set of snapshots of the Q-Ising model,it was demonstrated that optimal performance is achieved around the Bayes-optimal condition within statistical uncertainty and that the performance of the Bayes-optimal solution is superior to that of the maximum-a-posteriori(MAP) estimation which is a deterministic limit of the MPM estimate.These properties were qualitatively confirmed by the mean-field theory using an infinite-range model established in statistical mechanics.Additionally,a practical and useful method was constructed using the statistical mechanical iterative method via the Bethe approximation.Numerical simulations for a 256-grayscale standard image show that Bethe approximation works as good as the MPM estimation if the parameters are set appropriately.
