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. 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。
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. 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。
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. 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。
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. 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。
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. 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。