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Expectation-Maximization
The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
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模式识别课件
当预先不知道类型数目，或者用参数估计和非参数估计难以确定不同类型的类概率密度函数时，为了确定分类器的性能，可以利用聚类分析的方法。-When the pre-recognition software does not know the type of number, or parameter estimation and non-parameter estimation it is difficult to determine the different types of ca
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Mean Shift 这个概念最早是由Fukunaga等人[1]于1975年在一篇关于概率密度梯度函数的估计中提出来的,其最初含义正如其名,就是偏移的均值向量,在这里Mean Shift是一个名词,它指代的是一个向量,但随着Mean Shift理论的发展,Mean Shift的含义也发生了变化,如果我们说Mean Shift算法,一般是指一个迭代的步骤,即先算出当前点的偏移均值,移动该点到其偏移均值,然后以此为新的起始点,继续移动,直到满足一定的条件结束.-Mean Shift the conc
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虽然粒子滤波算法可以作为解决SLAM问题有效手段，但是该算法仍然存在着一些问题其中最主要的问题是需要用大量的样本数量能很好地近似系统的后验概率密度。-Although the particle filter can be used as an effective means to solve the SLAM problem, but the algorithm still exist some problems in which the most important issue is the
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虽然粒子滤波算法可以作为解决SLAM问题的有效手段，但是该算法仍然存在着一些问题。其中最主要的问题是需要用大量的样本数量才能很好地近似系统的后验概率密度。-Although the particle filter to solve the SLAM problem can be an effective means, but the algorithm still exist some problems. One of the most important issue is the need f
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虽然粒子滤波算法可以作为解决SLAM问题的有效手段，但是该算法仍然存在着一些问题。其中最主要的问题是需要用大量的样本数量才能很好地近似系统的后验概率密度。-Although the particle filter to solve the SLAM problem can be an effective means, but the algorithm still exist some problems. One of the most important issue is the need f
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虽然粒子滤波算法可以作为解决SLAM问题的有效手段，但是该算法仍然存在着一些问题。其中最主要的问题是需要用大量的样本数量才能很好地近似系统的后验概率密度。-Although the particle filter to solve the SLAM problem can be an effective means, but the algorithm still exist some problems. One of the most important issue is the need f
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贝叶斯决策理论：根据先验概率、类分布密度函数以及后验概率这些量来实现分类决策的方法.最小错误率的贝叶斯决策：根据一个事物后验概率最大作为分类依据的决策
-Bayesian decision theory: According to the a priori probability, the class distribution as well as the posterior probability density function of these values in order to a
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Probability Hypothesis Density filter versus Multiple
Hypothesis Tracking
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主要讲解了航空信道模型的仿真实现和参数选择，给出了各信道场景下的接收信号星座图以及概率密度曲线。-Mainly on the aviation simulation channel model implementation and parameter selection are given scenarios each channel received signal constellation and the probability density curve.
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An Analytical Model to Predict the Probability
Density Function of Elevation Angles for
LEO Satellite Systems-Based on earth-satellite geometry, an analytical
model is proposed to predict the probability density function
(pdf) of elevation an
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learn probability density function and its applications
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In probability theory, the normal (or Gaussian) distribution, is a continuous probability distribution that is often used as a first approximation to describe real-valued random variables that tend to cluster around a single mean value. The graph of
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matlab program for Lognormal distribution is a kind of probability density functions for any random variable whose logarithm is normally distributed it can be expressed as below.-matlab program for Lognormal distribution is a kind of probability dens
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Gaussian noise refers to its obey gaussian probability density function (i.e., normal distribution) of the noise. If a noise, its amplitude distribution obeys the gaussian distribution, and its power spectral density is uniformly distributed, has des
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所谓高斯噪声是指它的概率密度函数服从高斯分布（即正态分布）的一类噪声。如果一个噪声，它的幅度分布服从高斯分布，而它的功率谱密度又是均匀分布的，则称它为高斯白噪声。高斯白噪声的二阶矩不相关，一阶矩为常数，是指先后信号在时间上的相关性。高斯白噪声包括热噪声和散粒噪声。-Gaussian noise refers to its obey gaussian probability density function (i.e., normal distribution) of the noise. If
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所谓高斯噪声是指它的概率密度函数服从高斯分布（即正态分布）的一类噪声。如果一个噪声，它的幅度分布服从高斯分布，而它的功率谱密度又是均匀分布的，则称它为高斯白噪声。高斯白噪声的二阶矩不相关，一阶矩为常数，是指先后信号在时间上的相关性。高斯白噪声包括热噪声和散粒噪声。-Gaussian noise refers to its obey gaussian probability density function (i.e., normal distribution) of the noise. If
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所谓高斯噪声是指它的概率密度函数服从高斯分布（即正态分布）的一类噪声。如果一个噪声，它的幅度分布服从高斯分布，而它的功率谱密度又是均匀分布的，则称它为高斯白噪声。高斯白噪声的二阶矩不相关，一阶矩为常数，是指先后信号在时间上的相关性。高斯白噪声包括热噪声和散粒噪声。
-Gaussian noise refers to its obey gaussian probability density function (i.e., normal distribution) of the noise.
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probability density function plot
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研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度
函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了
基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明：与传统的短时谱估计算法相比，该算法不
仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知
质量。 -Recent research indicates that the speech spectral ampli
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