Please refer to the blog (https://blog.csdn.net/lkj345/article/details/50480317) for more details.
Bhattacharyya.m: Find the Bhattacharyya bound given means and covariances of single gaussian distributions.
CH2.mat: CH2 data.
CH2_1_a.m: Generate random vectors from the multivariate normal distribution.
CH2_1_a_test.m: Generate two normal distributions and plot.
CH2_1_b.m: Discriminant function of a normal distribution given prior probability.
CH2_1_c.m: Calculate the Euclidean distance between two vectors.
CH2_1_d.m: Calculate the Mahalanobis distance of a vector.
CH2_2.m: Generate classification model of two classes, then calculate the classification error and the Bhattacharyya bound.
CH2_2_test.m: Plot classification error and Bhattacharyya bound.
CH2_3.m: Plot classification error and Bhattacharyya bound.
CH2_4.m: Calculate the Mahalanobis distance with three classes, then classify the vectors.
CH2_4_test.m: Test CH2_4.m with four vectors.
CH2_5.m: The script is to prove that the average of a large number of independent random variables follows Gauss distribution.
CH2_6.m: Calculate the classification error of two classes.
CH2_6_test.m: Test CH2_6.m.
CH2_7.m: Calculate the Bhattacharyya bound, estimated error and a series of classification error of two Gauss distribution.
CH2_7_test.m: Test CH2_7.m.
CH2_8_a.m: Test CH2_7.m with two Gauss distribution.
CH2_8_b.m: Test CH2_7.m with two Gauss distribution.
CH2_8_c.m: Test CH2_7.m with two Gauss distribution.
Chernoff.m: Find the Chernoff bound given means and covariances of single gaussian distributions.