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In probability theory, a stochastic process, or sometimes random process, is the counterpart to a deterministic process (or deterministic system). Instead of dealing with only one possible reality of how the process might evolve under time (as is the
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probability and random proce-probability and random process
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粒子滤波源代码，通过寻找一组在状态空间中传播的随机样本来近似的表示概率密度函数，用样本均值代替积分运算，进而获得系统状态的最小方差估计的过程。-Particle filter source code, by finding a set of transmission in the state space representation of a random sample to approximate the probability density function, instead of usi
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targets and measurements as random finite sets and applying the
probability hypothesis density (PHD) recursion to propagate the
posterior intensity, which is a first order statistic of the random
finite set of targets, in time. At present, ther
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隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process
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（1）产生N（0,1）正态分布的随机过程；
（2）产生正态分布随机过程外的任意一种随机过程；
（3）对随机过程的性能进行评估，包括概率密度函数、分布函数、均值、方差和相关函数。((1) The stochastic process of generating N(0,1) normal distribution;
(2) generating any random process outside the normal distribution random process;
(3) evalua
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