文件名称:Maximum-Likelihood-Estimation
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Maximum Likelihood Estimation
Step 1. : Estimate the mean vector and covariance of an arbitrary 3-class dataset with bi-variate Gaussian distribution by maximum likelihood estimation
An arbitrary 3-class dataset is given (by Dataset.mat) and the priors are equal.
Step 2. : If three arbitrary samples are given as follows, determine to which class each sample should belong.
Let arbitrary samples are as follows: Z_1=[50 50]^T, Z_2=[10 20]^T, Z_3=[0 20]^T-Maximum Likelihood Estimation
Step 1. : Estimate the mean vector and covariance of an arbitrary 3-class dataset with bi-variate Gaussian distribution by maximum likelihood estimation
An arbitrary 3-class dataset is given (by Dataset.mat) and the priors are equal.
Step 2. : If three arbitrary samples are given as follows, determine to which class each sample should belong.
Let arbitrary samples are as follows: Z_1=[50 50]^T, Z_2=[10 20]^T, Z_3=[0 20]^T
Step 1. : Estimate the mean vector and covariance of an arbitrary 3-class dataset with bi-variate Gaussian distribution by maximum likelihood estimation
An arbitrary 3-class dataset is given (by Dataset.mat) and the priors are equal.
Step 2. : If three arbitrary samples are given as follows, determine to which class each sample should belong.
Let arbitrary samples are as follows: Z_1=[50 50]^T, Z_2=[10 20]^T, Z_3=[0 20]^T-Maximum Likelihood Estimation
Step 1. : Estimate the mean vector and covariance of an arbitrary 3-class dataset with bi-variate Gaussian distribution by maximum likelihood estimation
An arbitrary 3-class dataset is given (by Dataset.mat) and the priors are equal.
Step 2. : If three arbitrary samples are given as follows, determine to which class each sample should belong.
Let arbitrary samples are as follows: Z_1=[50 50]^T, Z_2=[10 20]^T, Z_3=[0 20]^T
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下载文件列表
bayes_cls.m
Dataset.mat
Density_Estimation.m
hist2d.m
mahal_dist.m
MLE_gauss.m
parzen_2d.m
plotgaus.m
Results.mat