PW：概率权重

“概率权重”（Probability Weights）常缩写为PW，以便于快速书写和使用。这一术语在多学科领域中被广泛运用，尤其在涉及风险评估、统计建模与决策分析等综合性场景中。PW表示某个事件发生的可能性所占的比重，是量化不确定性的常用指标。

Probability Weights具体释义

  • 英文缩写：PW
  • 英语全称：Probability Weights
  • 中文意思：概率权重
  • 中文拼音：gài lǜ quán zhòng
  • 相关领域： pw 未分类的

Probability Weights的英文发音

例句

  1. A particle set, which is randomly sampled from probability function and has corresponding weights, is introduced to approach the posterior distribution. Therefore it can handle nonlinear and non-Gaussian model without any limits.
  2. 粒子滤波从概率密度函数上随机抽取一组附带相关权值的粒子集，用其来逼近后验概率密度，是一种基于递推计算的序列蒙特卡罗算法，从而不受非线性、非高斯模型的限制。
  3. Training this NN detector at some specified probability of false alarm and then adjusting the weights of the bias nodes, we can acquire the test statistics.
  4. 此方法只需在特定虚警概率以及噪声条件下对神经网络进行训练，再通过调整偏移节点的连接权值，就可以得到不同虚警概率条件下的检验统计量。
  5. And use the principle of multiplication to calculate the probability of indicators at all levels of the portfolio weights, as detailed in Appendix 4.3.
  6. 并利用概率乘法原理计算出各级指标的组合权重，详见附录4。
  7. The Monte Carlo particle filter algorithms in this paper use the concepts of sequential importance sampling. The base idea of particle filter is the approximation of relevant probability distributions using a set of discrete random samples with associated weights.
  8. 本文的这种蒙特卡罗粒子滤波算法是利用序列重要性采样的概念，用一系列离散的带权重随机样本近似相应的概率密度函数。
  9. In order to increase the detection speed, by using mixing particle sets to express the posterior probability distribution and adopting the Monte Carlo numerical methods to calculate the mixing weights, the fast fault detection algorithm is proposed based on the estimate window method.
  10. 为了提高故障检测的速度，采用混合粒子集表达后验概率分布，并由MonteCarlo数值方法优化得到各粒子集的加权值，据此提出了基于估计窗的快速故障检测算法。

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